maybe maybe

Cargo.lock

@@ -514,6 +514,20 @@ version = "0.7.6"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "a1d728cc89cf3aee9ff92b05e62b19ee65a02b5702cff7d5a377e32c6ae29d8d"
 
+[[package]]
+name = "cluster_membership"
+version = "0.0.1"
+dependencies = [
+ "anyhow",
+ "async-trait",
+ "futures-lite",
+ "futures-timer",
+ "libp2p",
+ "log",
+ "tokio",
+ "tracing-subscriber",
+]
+
 [[package]]
 name = "colorchoice"
 version = "1.0.4"
@@ -998,6 +1012,7 @@ dependencies = [
 name = "exo_pyo3_bindings"
 version = "0.0.1"
 dependencies = [
+ "cluster_membership",
  "delegate",
  "derive_more",
  "env_logger",
@@ -1030,6 +1045,12 @@ dependencies = [
  "syn 2.0.111",
 ]
 
+[[package]]
+name = "fastrand"
+version = "2.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be"
+
 [[package]]
 name = "ff"
 version = "0.13.1"
@@ -1138,7 +1159,10 @@ version = "2.6.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f78e10609fe0e0b3f4157ffab1876319b5b0db102a2c60dc4626306dc46b44ad"
 dependencies = [
+ "fastrand",
  "futures-core",
+ "futures-io",
+ "parking",
  "pin-project-lite",
 ]
 

Cargo.toml

@@ -4,6 +4,7 @@ members = [
     "rust/networking",
     "rust/exo_pyo3_bindings",
     "rust/util",
+    "rust/cluster_membership",
 ]
 
 [workspace.package]
@@ -25,6 +26,7 @@ opt-level = 3
 ## Crate members as common dependencies
 networking = { path = "rust/networking" }
 util = { path = "rust/util" }
+cluster_membership = { path = "rust/cluster_membership" }
 
 # Proc-macro authoring tools
 syn = "2.0"
@@ -62,6 +64,7 @@ frunk-enum-core = "0.3"
 # Async dependencies
 tokio = "1.46"
 futures = "0.3"
+futures-lite = "2.6.1"
 futures-util = "0.3"
 futures-timer = "3.0"
 

app/EXO/EXO/Services/NetworkSetupHelper.swift

@@ -18,9 +18,6 @@ enum NetworkSetupHelper {
 
         set -euo pipefail
 
-        # Wait for macOS to finish network setup after boot
-        sleep 30
-
         PREFS="/Library/Preferences/SystemConfiguration/preferences.plist"
 
         # Remove bridge0 interface
@@ -83,7 +80,7 @@ enum NetworkSetupHelper {
                 let alert = NSAlert()
                 alert.messageText = "EXO Network Configuration"
                 alert.informativeText =
-                    "EXO needs to install a system service to configure local networking. This will disable Thunderbolt Bridge (preventing packet storms) and install a Network Location.\n\nYou will be prompted for your password."
+                    "EXO needs to install a system service to automatically disable Thunderbolt Bridge on startup. This prevents network loops when connecting multiple Macs via Thunderbolt.\n\nYou will be prompted for your administrator password."
                 alert.alertStyle = .informational
                 alert.addButton(withTitle: "Install")
                 alert.addButton(withTitle: "Not Now")

pyproject.toml

@@ -17,9 +17,9 @@ dependencies = [
     "loguru>=0.7.3",
     "exo_pyo3_bindings", # rust bindings
     "anyio==4.11.0",
-    "mlx @ git+https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git; sys_platform == 'darwin'",
+    "mlx==0.30.3; sys_platform == 'darwin'",
     "mlx[cpu]==0.30.3; sys_platform == 'linux'",
-    "mlx-lm==0.30.5",
+    "mlx-lm @ git+https://github.com/AlexCheema/mlx-lm.git@fix-transformers-5.0.0rc2",
     "tiktoken>=0.12.0", # required for kimi k2 tokenizer
     "hypercorn>=0.18.0",
     "openai-harmony>=0.0.8",

rust/cluster_membership/Cargo.toml

@@ -0,0 +1,23 @@
+[package]
+name = "cluster_membership"
+version.workspace = true
+edition.workspace = true
+publish = false
+
+[dependencies]
+# util
+anyhow.workspace = true
+log.workspace = true
+tracing-subscriber = { version = "0.3.19", features = ["default", "env-filter"] }
+
+# async
+tokio = { workspace = true, features = ["full"] }
+futures-timer = { workspace = true }
+futures-lite = "2.6.1"
+
+# networking
+libp2p = { workspace = true, features = ["full"] }
+async-trait = "0.1.89"
+
+[lints]
+workspace = true

rust/cluster_membership/examples/chatroom.rs

@@ -0,0 +1,30 @@
+use cluster_membership::Peer;
+use libp2p::identity::ed25519::SecretKey;
+use tokio::io::{self, AsyncBufReadExt};
+use tracing_subscriber::{EnvFilter, filter::LevelFilter};
+
+#[tokio::main]
+async fn main() {
+    let _ = tracing_subscriber::fmt()
+        .with_env_filter(EnvFilter::from_default_env().add_directive(LevelFilter::INFO.into()))
+        .try_init();
+
+    let (mut peer, send, mut recv) =
+        Peer::new(SecretKey::generate(), "hello".to_string()).expect("peer should always build");
+
+    let ch = peer.subscribe("chatroom".to_string());
+    let jh = tokio::spawn(async move { peer.run().await });
+
+    let mut stdin = io::BufReader::new(io::stdin()).lines();
+    loop {
+        tokio::select! {
+            Ok(Some(line)) = stdin.next_line() => {send.send((ch.clone(), line.into_bytes())).await.expect("example");}
+            Some(r) = recv.recv() => match r {
+                Ok((_, id, line)) => println!("{:?}:{:?}", id, String::from_utf8_lossy(&line)),
+                Err(e) => eprintln!("{e:?}"),
+            },
+            else => break
+        }
+    }
+    jh.await.expect("task failure");
+}

rust/cluster_membership/src/lib.rs

@@ -0,0 +1,227 @@
+use libp2p::{
+    Multiaddr, PeerId, Swarm, SwarmBuilder,
+    futures::StreamExt,
+    gossipsub::{self, PublishError, Sha256Topic, TopicHash},
+    identify,
+    identity::{Keypair, ed25519},
+    mdns,
+    swarm::{NetworkBehaviour, SwarmEvent, dial_opts::DialOpts},
+};
+use std::{
+    collections::HashMap,
+    time::{Duration, Instant},
+};
+use tokio::{select, sync::mpsc};
+
+const DEFAULT_BUFFER_SIZE: usize = 10;
+const MDNS_IGNORE_DURATION_SECS: u64 = 30;
+
+impl Peer {
+    pub fn new(
+        identity: ed25519::SecretKey,
+        namespace: String,
+    ) -> anyhow::Result<(
+        Self,
+        mpsc::Sender<(TopicHash, Vec<u8>)>,
+        mpsc::Receiver<Result<(TopicHash, PeerId, Vec<u8>), PublishError>>,
+    )> {
+        let mut id_bytes = identity.as_ref().to_vec();
+
+        let mut swarm =
+            SwarmBuilder::with_existing_identity(Keypair::ed25519_from_bytes(&mut id_bytes)?)
+                .with_tokio()
+                .with_quic()
+                // TODO(evan): .with_bandwidth_metrics();
+                .with_behaviour(|kp| Behaviour::new(kp, namespace.clone()))?
+                .build();
+
+        swarm.listen_on("/ip6/::/udp/0/quic-v1".parse()?)?;
+        swarm.listen_on("/ip4/0.0.0.0/udp/0/quic-v1".parse()?)?;
+        let (to_swarm, from_client) = mpsc::channel(DEFAULT_BUFFER_SIZE);
+        let (to_client, from_swarm) = mpsc::channel(DEFAULT_BUFFER_SIZE);
+        Ok((
+            Self {
+                swarm,
+                namespace,
+                denied: HashMap::new(),
+                from_client,
+                to_client,
+            },
+            to_swarm,
+            from_swarm,
+        ))
+    }
+
+    pub fn subscribe(&mut self, topic: String) -> TopicHash {
+        let topic = Sha256Topic::new(topic);
+        self.swarm
+            .behaviour_mut()
+            .gossipsub
+            .subscribe(&topic)
+            .expect("topic filtered");
+        topic.hash()
+    }
+
+    pub async fn run(&mut self) {
+        loop {
+            select! {
+                ev = self.swarm.select_next_some() => {
+                    let Ok(()) = self.handle_swarm_event(ev).await else {
+                        return
+                    };
+                },
+                Some(msg) = self.from_client.recv() => {
+                    if let Err(e) = self.swarm.behaviour_mut().gossipsub.publish(msg.0, msg.1) {
+                        let Ok(()) = self.to_client.send(Err(e)).await else {
+                            return
+                        };
+                    }
+                },
+            }
+        }
+    }
+
+    async fn handle_swarm_event(&mut self, event: SwarmEvent<BehaviourEvent>) -> Result<(), ()> {
+        let SwarmEvent::Behaviour(event) = event else {
+            if let SwarmEvent::NewListenAddr {
+                listener_id: _,
+                address,
+            } = event
+            {
+                log::info!("new listen address {address}")
+            }
+            return Ok(());
+        };
+        match event {
+            BehaviourEvent::Mdns(mdns_event) => match mdns_event {
+                mdns::Event::Discovered(vec) => {
+                    // Dial everyone
+                    let mut addrs = HashMap::<PeerId, Vec<Multiaddr>>::new();
+                    vec.into_iter()
+                        .filter(|(peer_id, _)| {
+                            self.denied.get(peer_id).is_none_or(|t| {
+                                t.elapsed() > Duration::from_secs(MDNS_IGNORE_DURATION_SECS)
+                            })
+                        })
+                        .for_each(|(peer_id, addr)| addrs.entry(peer_id).or_default().push(addr));
+                    addrs.into_iter().for_each(|(peer_id, addrs)| {
+                        let _ = self
+                            .swarm
+                            .dial(DialOpts::peer_id(peer_id).addresses(addrs).build());
+                    });
+                }
+                mdns::Event::Expired(vec) => {
+                    vec.iter().for_each(|(peer_id, _)| {
+                        log::debug!("{peer_id} no longer reachable on mDNS");
+                        self.swarm
+                            .behaviour_mut()
+                            .gossipsub
+                            .remove_explicit_peer(peer_id);
+                    });
+                }
+            },
+            BehaviourEvent::Identify(identify::Event::Received {
+                connection_id: _,
+                peer_id,
+                info,
+            }) => {
+                if info
+                    .protocols
+                    .iter()
+                    .any(|p| p.as_ref().contains(&self.namespace))
+                {
+                    self.passed_namespace(peer_id);
+                } else {
+                    self.failed_namespace(peer_id);
+                }
+            }
+            BehaviourEvent::Gossipsub(gossipsub::Event::Message {
+                propagation_source: _,
+                message_id: _,
+                message:
+                    gossipsub::Message {
+                        topic,
+                        data,
+                        source: Some(source_peer),
+                        ..
+                    },
+            }) => {
+                let Ok(()) = self.to_client.send(Ok((topic, source_peer, data))).await else {
+                    return Err(());
+                };
+            }
+            _ => {}
+        }
+        Ok(())
+    }
+
+    fn passed_namespace(&mut self, peer: PeerId) {
+        log::info!("new peer {peer:?}");
+        self.denied.remove(&peer);
+        self.swarm
+            .behaviour_mut()
+            .gossipsub
+            .remove_blacklisted_peer(&peer);
+        self.swarm
+            .behaviour_mut()
+            .gossipsub
+            .add_explicit_peer(&peer);
+    }
+
+    fn failed_namespace(&mut self, peer: PeerId) {
+        log::debug!("{peer} failed handshake");
+        self.denied.insert(peer, Instant::now());
+        self.swarm.behaviour_mut().gossipsub.blacklist_peer(&peer);
+        // we don't care if disconnect fails
+        let _ = self.swarm.disconnect_peer_id(peer);
+    }
+}
+
+pub struct Peer {
+    pub swarm: Swarm<Behaviour>,
+    denied: HashMap<PeerId, Instant>,
+    namespace: String,
+    to_client: mpsc::Sender<Result<(TopicHash, PeerId, Vec<u8>), PublishError>>,
+    from_client: mpsc::Receiver<(TopicHash, Vec<u8>)>,
+}
+
+#[test]
+fn foo() {
+    fn bar<T: Send>(t: T) {}
+    let p: Peer = unimplemented!();
+    bar(p);
+}
+
+#[derive(NetworkBehaviour)]
+pub struct Behaviour {
+    mdns: mdns::tokio::Behaviour,
+    pub gossipsub: gossipsub::Behaviour,
+    identify: identify::Behaviour,
+}
+
+impl Behaviour {
+    fn new(kp: &Keypair, namespace: String) -> Self {
+        let mdns = mdns::tokio::Behaviour::new(Default::default(), kp.public().to_peer_id())
+            .expect("implementation is infallible");
+        let gossipsub = gossipsub::Behaviour::new(
+            gossipsub::MessageAuthenticity::Signed(kp.clone()),
+            gossipsub::ConfigBuilder::default()
+                .max_transmit_size(1024 * 1024)
+                .protocol_id_prefix(format!("/exo/gossip/{namespace}/v1"))
+                .build()
+                .expect("fixed gossipsub config should always build"),
+        )
+        .expect("fixed gossipsub init should always build");
+
+        let identify = identify::Behaviour::new(
+            identify::Config::new_with_signed_peer_record(format!("/exo/identity/v1"), kp)
+                .with_push_listen_addr_updates(true),
+        );
+
+        Behaviour {
+            mdns,
+            gossipsub,
+            identify,
+        }
+    }
+}

rust/exo_pyo3_bindings/Cargo.toml

@@ -22,6 +22,7 @@ doc = false
 workspace = true
 
 [dependencies]
+cluster_membership.workspace = true
 networking = { workspace = true }
 
 # interop

rust/exo_pyo3_bindings/src/pylibp2p/mod.rs

@@ -6,3 +6,41 @@
 
 pub mod ident;
 pub mod multiaddr;
+
+use std::sync::Mutex;
+
+use cluster_membership::Peer;
+use libp2p::identity::ed25519::Keypair;
+use pyo3::prelude::*;
+use pyo3_stub_gen::derive::{gen_stub_pyclass, gen_stub_pymethods};
+
+#[gen_stub_pyclass]
+#[pyclass]
+#[derive(Clone)]
+pub struct PyKeypair(Keypair);
+
+#[gen_stub_pymethods]
+#[pymethods]
+impl PyKeypair {
+    #[staticmethod]
+    fn generate() -> Self {
+        Self(Keypair::generate())
+    }
+}
+
+#[gen_stub_pyclass]
+#[pyclass]
+pub struct PyPeer(Mutex<Peer>);
+
+#[gen_stub_pymethods]
+#[pymethods]
+impl PyPeer {
+    #[staticmethod]
+    fn init(kp: PyKeypair, namespace: String) -> PyResult<Self> {
+        Ok(PyPeer(Mutex::new(
+            Peer::new(kp.0.secret(), namespace)
+                .map_err(|e| e.pyerr())?
+                .0,
+        )))
+    }
+}

src/exo/download/download_utils.py

@@ -121,20 +121,11 @@ async def ensure_models_dir() -> Path:
 
 
 async def delete_model(model_id: ModelId) -> bool:
-    models_dir = await ensure_models_dir()
-    model_dir = models_dir / model_id.normalize()
-    cache_dir = models_dir / "caches" / model_id.normalize()
-
-    deleted = False
-    if await aios.path.exists(model_dir):
-        await asyncio.to_thread(shutil.rmtree, model_dir, ignore_errors=False)
-        deleted = True
-
-    # Also clear cache
-    if await aios.path.exists(cache_dir):
-        await asyncio.to_thread(shutil.rmtree, cache_dir, ignore_errors=False)
-
-    return deleted
+    model_dir = await ensure_models_dir() / model_id.normalize()
+    if not await aios.path.exists(model_dir):
+        return False
+    await asyncio.to_thread(shutil.rmtree, model_dir, ignore_errors=False)
+    return True
 
 
 async def seed_models(seed_dir: str | Path):
@@ -160,28 +151,16 @@ async def fetch_file_list_with_cache(
     target_dir = (await ensure_models_dir()) / "caches" / model_id.normalize()
     await aios.makedirs(target_dir, exist_ok=True)
     cache_file = target_dir / f"{model_id.normalize()}--{revision}--file_list.json"
-
-    # Always try fresh first
-    try:
-        file_list = await fetch_file_list_with_retry(
-            model_id, revision, recursive=recursive
-        )
-        # Update cache with fresh data
-        async with aiofiles.open(cache_file, "w") as f:
-            await f.write(
-                TypeAdapter(list[FileListEntry]).dump_json(file_list).decode()
-            )
-        return file_list
-    except Exception as e:
-        # Fetch failed - try cache fallback
-        if await aios.path.exists(cache_file):
-            logger.warning(
-                f"Failed to fetch file list for {model_id}, using cached data: {e}"
-            )
-            async with aiofiles.open(cache_file, "r") as f:
-                return TypeAdapter(list[FileListEntry]).validate_json(await f.read())
-        # No cache available, propagate the error
-        raise
+    if await aios.path.exists(cache_file):
+        async with aiofiles.open(cache_file, "r") as f:
+            return TypeAdapter(list[FileListEntry]).validate_json(await f.read())
+    file_list = await fetch_file_list_with_retry(
+        model_id, revision, recursive=recursive
+    )
+    await aios.makedirs(cache_file.parent, exist_ok=True)
+    async with aiofiles.open(cache_file, "w") as f:
+        await f.write(TypeAdapter(list[FileListEntry]).dump_json(file_list).decode())
+    return file_list
 
 
 async def fetch_file_list_with_retry(
@@ -353,28 +332,8 @@ async def _download_file(
     target_dir: Path,
     on_progress: Callable[[int, int, bool], None] = lambda _, __, ___: None,
 ) -> Path:
-    target_path = target_dir / path
-
-    if await aios.path.exists(target_path):
-        local_size = (await aios.stat(target_path)).st_size
-
-        # Try to verify against remote, but allow offline operation
-        try:
-            remote_size, _ = await file_meta(model_id, revision, path)
-            if local_size != remote_size:
-                logger.info(
-                    f"File {path} size mismatch (local={local_size}, remote={remote_size}), re-downloading"
-                )
-                await aios.remove(target_path)
-            else:
-                return target_path
-        except Exception as e:
-            # Offline or network error - trust local file
-            logger.debug(
-                f"Could not verify {path} against remote (offline?): {e}, using local file"
-            )
-            return target_path
-
+    if await aios.path.exists(target_dir / path):
+        return target_dir / path
     await aios.makedirs((target_dir / path).parent, exist_ok=True)
     length, etag = await file_meta(model_id, revision, path)
     remote_hash = etag[:-5] if etag.endswith("-gzip") else etag
@@ -583,26 +542,17 @@ async def download_shard(
     async def on_progress_wrapper(
         file: FileListEntry, curr_bytes: int, total_bytes: int, is_renamed: bool
     ) -> None:
-        previous_progress = file_progress.get(file.path)
-
-        # Detect re-download: curr_bytes < previous downloaded means file was deleted and restarted
-        is_redownload = (
-            previous_progress is not None
-            and curr_bytes < previous_progress.downloaded.in_bytes
+        start_time = (
+            file_progress[file.path].start_time
+            if file.path in file_progress
+            else time.time()
+        )
+        downloaded_this_session = (
+            file_progress[file.path].downloaded_this_session.in_bytes
+            + (curr_bytes - file_progress[file.path].downloaded.in_bytes)
+            if file.path in file_progress
+            else curr_bytes
         )
-
-        if is_redownload or previous_progress is None:
-            # Fresh download or re-download: reset tracking
-            start_time = time.time()
-            downloaded_this_session = curr_bytes
-        else:
-            # Continuing download: accumulate
-            start_time = previous_progress.start_time
-            downloaded_this_session = (
-                previous_progress.downloaded_this_session.in_bytes
-                + (curr_bytes - previous_progress.downloaded.in_bytes)
-            )
-
         speed = (
             downloaded_this_session / (time.time() - start_time)
             if time.time() - start_time > 0

src/exo/download/tests/test_download_verification.py

@@ -1,451 +0,0 @@
-"""Tests for download verification and cache behavior."""
-
-import time
-from collections.abc import AsyncIterator
-from datetime import timedelta
-from pathlib import Path
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import aiofiles
-import aiofiles.os as aios
-import pytest
-from pydantic import TypeAdapter
-
-from exo.download.download_utils import (
-    delete_model,
-    fetch_file_list_with_cache,
-)
-from exo.shared.types.common import ModelId
-from exo.shared.types.memory import Memory
-from exo.shared.types.worker.downloads import FileListEntry, RepoFileDownloadProgress
-
-
-@pytest.fixture
-def model_id() -> ModelId:
-    return ModelId("test-org/test-model")
-
-
-@pytest.fixture
-async def temp_models_dir(tmp_path: Path) -> AsyncIterator[Path]:
-    """Set up a temporary models directory for testing."""
-    models_dir = tmp_path / "models"
-    await aios.makedirs(models_dir, exist_ok=True)
-    with patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir):
-        yield models_dir
-
-
-class TestFileVerification:
-    """Tests for file size verification in _download_file."""
-
-    async def test_redownload_when_file_size_changes_upstream(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that files with mismatched sizes are re-downloaded."""
-        # Import inside test to allow patching
-        from exo.download.download_utils import (
-            _download_file,  # pyright: ignore[reportPrivateUsage]
-        )
-
-        target_dir = tmp_path / "downloads"
-        await aios.makedirs(target_dir, exist_ok=True)
-
-        # Create a local file with wrong size
-        local_file = target_dir / "test.safetensors"
-        async with aiofiles.open(local_file, "wb") as f:
-            await f.write(b"local content")  # 13 bytes
-
-        remote_size = 1000  # Different from local
-        remote_hash = "abc123"
-
-        with (
-            patch(
-                "exo.download.download_utils.file_meta",
-                new_callable=AsyncMock,
-                return_value=(remote_size, remote_hash),
-            ) as mock_file_meta,
-            patch(
-                "exo.download.download_utils.create_http_session"
-            ) as mock_session_factory,
-        ):
-            # Set up mock HTTP response for re-download
-            mock_response = MagicMock()
-            mock_response.status = 200
-            mock_response.content.read = AsyncMock(  # pyright: ignore[reportAny]
-                side_effect=[b"x" * remote_size, b""]
-            )
-
-            mock_session = MagicMock()
-            mock_session.get.return_value.__aenter__ = AsyncMock(  # pyright: ignore[reportAny]
-                return_value=mock_response
-            )
-            mock_session.get.return_value.__aexit__ = AsyncMock(  # pyright: ignore[reportAny]
-                return_value=None
-            )
-            mock_session_factory.return_value.__aenter__ = AsyncMock(  # pyright: ignore[reportAny]
-                return_value=mock_session
-            )
-            mock_session_factory.return_value.__aexit__ = AsyncMock(  # pyright: ignore[reportAny]
-                return_value=None
-            )
-
-            # Mock calc_hash to return the expected hash
-            with patch(
-                "exo.download.download_utils.calc_hash",
-                new_callable=AsyncMock,
-                return_value=remote_hash,
-            ):
-                await _download_file(model_id, "main", "test.safetensors", target_dir)
-
-            # file_meta should be called twice: once for verification, once for download
-            assert mock_file_meta.call_count == 2
-
-    async def test_skip_download_when_file_size_matches(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that files with matching sizes are not re-downloaded."""
-        from exo.download.download_utils import (
-            _download_file,  # pyright: ignore[reportPrivateUsage]
-        )
-
-        target_dir = tmp_path / "downloads"
-        await aios.makedirs(target_dir, exist_ok=True)
-
-        # Create a local file
-        local_file = target_dir / "test.safetensors"
-        local_content = b"local content"
-        async with aiofiles.open(local_file, "wb") as f:
-            await f.write(local_content)
-
-        remote_size = len(local_content)  # Same as local
-        remote_hash = "abc123"
-
-        with (
-            patch(
-                "exo.download.download_utils.file_meta",
-                new_callable=AsyncMock,
-                return_value=(remote_size, remote_hash),
-            ) as mock_file_meta,
-            patch(
-                "exo.download.download_utils.create_http_session"
-            ) as mock_session_factory,
-        ):
-            result = await _download_file(
-                model_id, "main", "test.safetensors", target_dir
-            )
-
-            # Should return immediately without downloading
-            assert result == local_file
-            mock_file_meta.assert_called_once()
-            mock_session_factory.assert_not_called()
-
-    async def test_offline_fallback_uses_local_file(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that local files are used when network is unavailable."""
-        from exo.download.download_utils import (
-            _download_file,  # pyright: ignore[reportPrivateUsage]
-        )
-
-        target_dir = tmp_path / "downloads"
-        await aios.makedirs(target_dir, exist_ok=True)
-
-        # Create a local file
-        local_file = target_dir / "test.safetensors"
-        async with aiofiles.open(local_file, "wb") as f:
-            await f.write(b"local content")
-
-        with (
-            patch(
-                "exo.download.download_utils.file_meta",
-                new_callable=AsyncMock,
-                side_effect=Exception("Network error"),
-            ),
-            patch(
-                "exo.download.download_utils.create_http_session"
-            ) as mock_session_factory,
-        ):
-            result = await _download_file(
-                model_id, "main", "test.safetensors", target_dir
-            )
-
-            # Should return local file without attempting download
-            assert result == local_file
-            mock_session_factory.assert_not_called()
-
-
-class TestFileListCache:
-    """Tests for file list caching behavior."""
-
-    async def test_fetch_fresh_and_update_cache(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that fresh data is fetched and cache is updated."""
-        models_dir = tmp_path / "models"
-
-        file_list = [
-            FileListEntry(type="file", path="model.safetensors", size=1000),
-            FileListEntry(type="file", path="config.json", size=100),
-        ]
-
-        with (
-            patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir),
-            patch(
-                "exo.download.download_utils.fetch_file_list_with_retry",
-                new_callable=AsyncMock,
-                return_value=file_list,
-            ) as mock_fetch,
-        ):
-            result = await fetch_file_list_with_cache(model_id, "main")
-
-            assert result == file_list
-            mock_fetch.assert_called_once()
-
-            # Verify cache was written
-            cache_file = (
-                models_dir
-                / "caches"
-                / model_id.normalize()
-                / f"{model_id.normalize()}--main--file_list.json"
-            )
-            assert await aios.path.exists(cache_file)
-
-            async with aiofiles.open(cache_file, "r") as f:
-                cached_data = TypeAdapter(list[FileListEntry]).validate_json(
-                    await f.read()
-                )
-            assert cached_data == file_list
-
-    async def test_fallback_to_cache_when_fetch_fails(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that cached data is used when fetch fails."""
-        models_dir = tmp_path / "models"
-        cache_dir = models_dir / "caches" / model_id.normalize()
-        await aios.makedirs(cache_dir, exist_ok=True)
-
-        # Create cache file
-        cached_file_list = [
-            FileListEntry(type="file", path="model.safetensors", size=1000),
-        ]
-        cache_file = cache_dir / f"{model_id.normalize()}--main--file_list.json"
-        async with aiofiles.open(cache_file, "w") as f:
-            await f.write(
-                TypeAdapter(list[FileListEntry]).dump_json(cached_file_list).decode()
-            )
-
-        with (
-            patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir),
-            patch(
-                "exo.download.download_utils.fetch_file_list_with_retry",
-                new_callable=AsyncMock,
-                side_effect=Exception("Network error"),
-            ),
-        ):
-            result = await fetch_file_list_with_cache(model_id, "main")
-
-            assert result == cached_file_list
-
-    async def test_error_propagates_when_no_cache(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that errors propagate when fetch fails and no cache exists."""
-        models_dir = tmp_path / "models"
-
-        with (
-            patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir),
-            patch(
-                "exo.download.download_utils.fetch_file_list_with_retry",
-                new_callable=AsyncMock,
-                side_effect=Exception("Network error"),
-            ),
-            pytest.raises(Exception, match="Network error"),
-        ):
-            await fetch_file_list_with_cache(model_id, "main")
-
-
-class TestModelDeletion:
-    """Tests for model deletion including cache cleanup."""
-
-    async def test_delete_model_clears_cache(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test that deleting a model also deletes its cache."""
-        models_dir = tmp_path / "models"
-        model_dir = models_dir / model_id.normalize()
-        cache_dir = models_dir / "caches" / model_id.normalize()
-
-        # Create model and cache directories
-        await aios.makedirs(model_dir, exist_ok=True)
-        await aios.makedirs(cache_dir, exist_ok=True)
-
-        # Add some files
-        async with aiofiles.open(model_dir / "model.safetensors", "w") as f:
-            await f.write("model data")
-        async with aiofiles.open(cache_dir / "file_list.json", "w") as f:
-            await f.write("[]")
-
-        with patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir):
-            result = await delete_model(model_id)
-
-            assert result is True
-            assert not await aios.path.exists(model_dir)
-            assert not await aios.path.exists(cache_dir)
-
-    async def test_delete_model_only_cache_exists(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test deleting when only cache exists (model already deleted)."""
-        models_dir = tmp_path / "models"
-        cache_dir = models_dir / "caches" / model_id.normalize()
-
-        # Only create cache directory
-        await aios.makedirs(cache_dir, exist_ok=True)
-        async with aiofiles.open(cache_dir / "file_list.json", "w") as f:
-            await f.write("[]")
-
-        with patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir):
-            result = await delete_model(model_id)
-
-            # Returns False because model dir didn't exist
-            assert result is False
-            # But cache should still be cleaned up
-            assert not await aios.path.exists(cache_dir)
-
-    async def test_delete_nonexistent_model(
-        self, model_id: ModelId, tmp_path: Path
-    ) -> None:
-        """Test deleting a model that doesn't exist."""
-        models_dir = tmp_path / "models"
-        await aios.makedirs(models_dir, exist_ok=True)
-
-        with patch("exo.download.download_utils.EXO_MODELS_DIR", models_dir):
-            result = await delete_model(model_id)
-
-            assert result is False
-
-
-class TestProgressResetOnRedownload:
-    """Tests for progress tracking when files are re-downloaded."""
-
-    async def test_progress_resets_correctly_on_redownload(
-        self, model_id: ModelId
-    ) -> None:
-        """Test that progress tracking resets when a file is re-downloaded.
-
-        When a file is deleted and re-downloaded (due to size mismatch),
-        the progress tracking should reset rather than calculating negative
-        downloaded_this_session values.
-        """
-        # Simulate file_progress dict as it exists in download_shard
-        file_progress: dict[str, RepoFileDownloadProgress] = {}
-
-        # Initialize with old file progress (simulating existing large file)
-        old_file_size = 1_500_000_000  # 1.5 GB
-        file_progress["model.safetensors"] = RepoFileDownloadProgress(
-            repo_id=model_id,
-            repo_revision="main",
-            file_path="model.safetensors",
-            downloaded=Memory.from_bytes(old_file_size),
-            downloaded_this_session=Memory.from_bytes(0),
-            total=Memory.from_bytes(old_file_size),
-            speed=0,
-            eta=timedelta(0),
-            status="not_started",
-            start_time=time.time() - 10,  # Started 10 seconds ago
-        )
-
-        # Simulate the logic from on_progress_wrapper after re-download starts
-        # This is the exact logic from the fixed on_progress_wrapper
-        curr_bytes = 100_000  # 100 KB - new download just started
-        previous_progress = file_progress.get("model.safetensors")
-
-        # Detect re-download: curr_bytes < previous downloaded
-        is_redownload = (
-            previous_progress is not None
-            and curr_bytes < previous_progress.downloaded.in_bytes
-        )
-
-        if is_redownload or previous_progress is None:
-            # Fresh download or re-download: reset tracking
-            start_time = time.time()
-            downloaded_this_session = curr_bytes
-        else:
-            # Continuing download: accumulate
-            start_time = previous_progress.start_time
-            downloaded_this_session = (
-                previous_progress.downloaded_this_session.in_bytes
-                + (curr_bytes - previous_progress.downloaded.in_bytes)
-            )
-
-        # Key assertions
-        assert is_redownload is True, "Should detect re-download scenario"
-        assert downloaded_this_session == curr_bytes, (
-            "downloaded_this_session should equal curr_bytes on re-download"
-        )
-        assert downloaded_this_session > 0, (
-            "downloaded_this_session should be positive, not negative"
-        )
-
-        # Calculate speed (should be positive)
-        elapsed = time.time() - start_time
-        speed = downloaded_this_session / elapsed if elapsed > 0 else 0
-        assert speed >= 0, "Speed should be non-negative"
-
-    async def test_progress_accumulates_on_continuing_download(
-        self, model_id: ModelId
-    ) -> None:
-        """Test that progress accumulates correctly for continuing downloads.
-
-        When a download continues from where it left off (resume),
-        the progress should accumulate correctly.
-        """
-        file_progress: dict[str, RepoFileDownloadProgress] = {}
-
-        # Initialize with partial download progress
-        initial_downloaded = 500_000  # 500 KB already downloaded
-        start_time = time.time() - 5  # Started 5 seconds ago
-        file_progress["model.safetensors"] = RepoFileDownloadProgress(
-            repo_id=model_id,
-            repo_revision="main",
-            file_path="model.safetensors",
-            downloaded=Memory.from_bytes(initial_downloaded),
-            downloaded_this_session=Memory.from_bytes(initial_downloaded),
-            total=Memory.from_bytes(1_000_000),
-            speed=100_000,
-            eta=timedelta(seconds=5),
-            status="in_progress",
-            start_time=start_time,
-        )
-
-        # Progress callback with more bytes downloaded
-        curr_bytes = 600_000  # 600 KB - continuing download
-        previous_progress = file_progress.get("model.safetensors")
-
-        # This is NOT a re-download (curr_bytes > previous downloaded)
-        is_redownload = (
-            previous_progress is not None
-            and curr_bytes < previous_progress.downloaded.in_bytes
-        )
-
-        if is_redownload or previous_progress is None:
-            downloaded_this_session = curr_bytes
-            used_start_time = time.time()
-        else:
-            used_start_time = previous_progress.start_time
-            downloaded_this_session = (
-                previous_progress.downloaded_this_session.in_bytes
-                + (curr_bytes - previous_progress.downloaded.in_bytes)
-            )
-
-        # Key assertions
-        assert is_redownload is False, (
-            "Should NOT detect re-download for continuing download"
-        )
-        assert used_start_time == start_time, "Should preserve original start_time"
-        expected_session = initial_downloaded + (curr_bytes - initial_downloaded)
-        assert downloaded_this_session == expected_session, (
-            f"Should accumulate: {downloaded_this_session} == {expected_session}"
-        )
-        assert downloaded_this_session == 600_000, (
-            "downloaded_this_session should equal total downloaded so far"
-        )

src/exo/shared/models/model_cards.py

@@ -413,9 +413,9 @@ MODEL_CARDS: dict[str, ModelCard] = {
     ),
 }
 
-_IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
+_IMAGE_MODEL_CARDS: dict[str, ModelCard] = {
     "flux1-schnell": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-schnell"),
+        model_id=ModelId("black-forest-labs/FLUX.1-schnell"),
         storage_size=Memory.from_bytes(23782357120 + 9524621312),
         n_layers=57,
         hidden_size=1,
@@ -428,7 +428,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
                 storage_size=Memory.from_kb(0),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,
+                safetensors_index_filename=None,  # Single file
             ),
             ComponentInfo(
                 component_name="text_encoder_2",
@@ -442,7 +442,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
                 component_name="transformer",
                 component_path="transformer/",
                 storage_size=Memory.from_bytes(23782357120),
-                n_layers=57,
+                n_layers=57,  # 19 transformer_blocks + 38 single_transformer_blocks
                 can_shard=True,
                 safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
             ),
@@ -457,7 +457,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
         ],
     ),
     "flux1-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-dev"),
+        model_id=ModelId("black-forest-labs/FLUX.1-dev"),
         storage_size=Memory.from_bytes(23782357120 + 9524621312),
         n_layers=57,
         hidden_size=1,
@@ -470,7 +470,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
                 storage_size=Memory.from_kb(0),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,
+                safetensors_index_filename=None,  # Single file
             ),
             ComponentInfo(
                 component_name="text_encoder_2",
@@ -484,7 +484,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
                 component_name="transformer",
                 component_path="transformer/",
                 storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
+                n_layers=57,  # 19 transformer_blocks + 38 single_transformer_blocks
                 can_shard=True,
                 safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
             ),
@@ -499,7 +499,7 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
         ],
     ),
     "flux1-krea-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-Krea-dev"),
+        model_id=ModelId("black-forest-labs/FLUX.1-Krea-dev"),
         storage_size=Memory.from_bytes(23802816640 + 9524621312),  # Same as dev
         n_layers=57,
         hidden_size=1,
@@ -541,9 +541,9 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
         ],
     ),
     "qwen-image": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image"),
+        model_id=ModelId("Qwen/Qwen-Image"),
         storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
+        n_layers=60,  # Qwen has 60 transformer blocks (all joint-style)
         hidden_size=1,
         supports_tensor=False,
         tasks=[ModelTask.TextToImage],
@@ -551,10 +551,10 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
             ComponentInfo(
                 component_name="text_encoder",
                 component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
+                storage_size=Memory.from_kb(16584333312),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,
+                safetensors_index_filename=None,  # Single file
             ),
             ComponentInfo(
                 component_name="transformer",
@@ -575,9 +575,9 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
         ],
     ),
     "qwen-image-edit-2509": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image-Edit-2509"),
+        model_id=ModelId("Qwen/Qwen-Image-Edit-2509"),
         storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
+        n_layers=60,  # Qwen has 60 transformer blocks (all joint-style)
         hidden_size=1,
         supports_tensor=False,
         tasks=[ModelTask.ImageToImage],
@@ -585,10 +585,10 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
             ComponentInfo(
                 component_name="text_encoder",
                 component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
+                storage_size=Memory.from_kb(16584333312),
                 n_layers=12,
                 can_shard=False,
-                safetensors_index_filename=None,
+                safetensors_index_filename=None,  # Single file
             ),
             ComponentInfo(
                 component_name="transformer",
@@ -610,92 +610,6 @@ _IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
     ),
 }
 
-
-def _generate_image_model_quant_variants(
-    base_name: str,
-    base_card: ModelCard,
-) -> dict[str, ModelCard]:
-    """Create quantized variants of an image model card.
-
-    Only the transformer component is quantized; text encoders stay at bf16.
-    Sizes are calculated exactly from the base card's component sizes.
-    """
-    if base_card.components is None:
-        raise ValueError(f"Image model {base_name} must have components defined")
-
-    # quantizations = [8, 6, 5, 4, 3]
-    quantizations = [8, 4]
-
-    num_transformer_bytes = next(
-        c.storage_size.in_bytes
-        for c in base_card.components
-        if c.component_name == "transformer"
-    )
-
-    transformer_bytes = Memory.from_bytes(num_transformer_bytes)
-
-    remaining_bytes = Memory.from_bytes(
-        sum(
-            c.storage_size.in_bytes
-            for c in base_card.components
-            if c.component_name != "transformer"
-        )
-    )
-
-    def with_transformer_size(new_size: Memory) -> list[ComponentInfo]:
-        assert base_card.components is not None
-        return [
-            ComponentInfo(
-                component_name=c.component_name,
-                component_path=c.component_path,
-                storage_size=new_size
-                if c.component_name == "transformer"
-                else c.storage_size,
-                n_layers=c.n_layers,
-                can_shard=c.can_shard,
-                safetensors_index_filename=c.safetensors_index_filename,
-            )
-            for c in base_card.components
-        ]
-
-    variants = {
-        base_name: ModelCard(
-            model_id=base_card.model_id,
-            storage_size=transformer_bytes + remaining_bytes,
-            n_layers=base_card.n_layers,
-            hidden_size=base_card.hidden_size,
-            supports_tensor=base_card.supports_tensor,
-            tasks=base_card.tasks,
-            components=with_transformer_size(transformer_bytes),
-        )
-    }
-
-    for quant in quantizations:
-        quant_transformer_bytes = Memory.from_bytes(
-            (num_transformer_bytes * quant) // 16
-        )
-        total_bytes = remaining_bytes + quant_transformer_bytes
-
-        model_id = ModelId(base_card.model_id + f"-{quant}bit")
-
-        variants[f"{base_name}-{quant}bit"] = ModelCard(
-            model_id=model_id,
-            storage_size=total_bytes,
-            n_layers=base_card.n_layers,
-            hidden_size=base_card.hidden_size,
-            supports_tensor=base_card.supports_tensor,
-            tasks=base_card.tasks,
-            components=with_transformer_size(quant_transformer_bytes),
-        )
-
-    return variants
-
-
-_image_model_cards: dict[str, ModelCard] = {}
-for _base_name, _base_card in _IMAGE_BASE_MODEL_CARDS.items():
-    _image_model_cards |= _generate_image_model_quant_variants(_base_name, _base_card)
-_IMAGE_MODEL_CARDS = _image_model_cards
-
 if EXO_ENABLE_IMAGE_MODELS:
     MODEL_CARDS.update(_IMAGE_MODEL_CARDS)
 

src/exo/shared/types/mlx.py

@@ -1,12 +0,0 @@
-"""Shared types for MLX-related functionality."""
-
-from collections.abc import Sequence
-
-from mlx_lm.models.cache import (
-    KVCache,
-    QuantizedKVCache,
-    RotatingKVCache,
-)
-
-# This list contains one cache entry per transformer layer
-KVCacheType = Sequence[KVCache | RotatingKVCache | QuantizedKVCache]

src/exo/worker/engines/mlx/auto_parallel.py

@@ -19,8 +19,6 @@ from mlx_lm.models.deepseek_v32 import DeepseekV32MLP
 from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model
 from mlx_lm.models.glm4_moe import Model as Glm4MoeModel
 from mlx_lm.models.glm4_moe import MoE
-from mlx_lm.models.glm4_moe_lite import Glm4MoeLiteDecoderLayer, Glm4MoeLiteMLP
-from mlx_lm.models.glm4_moe_lite import Model as GLM4MoeLiteModel
 from mlx_lm.models.gpt_oss import GptOssMoeModel
 from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.models.llama import Model as LlamaModel
@@ -147,10 +145,6 @@ class PipelineLastLayer(CustomMlxLayer):
             if cache is not None:
                 cache.keys = mx.depends(cache.keys, output)  # type: ignore[reportUnknownMemberType]
 
-        output = mx.distributed.all_gather(output, group=self.group)[
-            -output.shape[0] :
-        ]  # type :ignore
-
         return output
 
 
@@ -258,6 +252,10 @@ def patch_pipeline_model[T](model: T, group: mx.distributed.Group) -> T:
         if cache is not None:
             cache[-1].state = mx.depends(cache[-1].state, logits)  # type: ignore
 
+        logits = mx.distributed.all_gather(logits, group=group)[
+            -logits.shape[0] :
+        ]  # type :ignore
+
         return logits
 
     cls.__call__ = patched_call
@@ -336,7 +334,15 @@ def tensor_auto_parallel(
         group=group,
     )
 
+    if hasattr(model, "shard") and not isinstance(model, GptOssModel):
+        try:
+            model.shard(group)  # type: ignore
+            return patch_tensor_model(model)
+        except (AttributeError, TypeError, NameError):
+            pass
+
     if isinstance(model, (LlamaModel, Ministral3Model)):
+        logger.warning("shouldn't be hit - upstream sharding exists")
         tensor_parallel_sharding_strategy = LlamaShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -345,6 +351,7 @@ def tensor_auto_parallel(
             sharded_to_all_linear_in_place,
         )
     elif isinstance(model, (DeepseekV3Model, DeepseekV32Model)):
+        logger.warning("shouldn't be hit - upstream sharding exists")
         tensor_parallel_sharding_strategy = DeepSeekShardingStrategy(
             group,
             all_to_sharded_linear,
@@ -360,14 +367,6 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
-    elif isinstance(model, GLM4MoeLiteModel):
-        tensor_parallel_sharding_strategy = GLM4MoeLiteShardingStrategy(
-            group,
-            all_to_sharded_linear,
-            sharded_to_all_linear,
-            all_to_sharded_linear_in_place,
-            sharded_to_all_linear_in_place,
-        )
     elif isinstance(model, (Qwen3MoeModel, Glm4MoeModel, Qwen3NextModel)):
         tensor_parallel_sharding_strategy = QwenShardingStrategy(
             group,
@@ -442,7 +441,7 @@ class LlamaShardingStrategy(TensorParallelShardingStrategy):
             layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
             layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
             layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
-            mx.eval(layer)
+
         return model
 
 
@@ -517,8 +516,6 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
                 layer.mlp = ShardedDeepseekV3MoE(layer.mlp)  # type: ignore
                 layer.mlp.sharding_group = self.group
 
-            mx.eval(layer)
-
         return model
 
 
@@ -536,84 +533,6 @@ class ShardedDeepseekV3MoE(CustomMlxLayer):
         return y
 
 
-class GLM4MoeLiteShardingStrategy(TensorParallelShardingStrategy):
-    def shard_model(
-        self,
-        model: nn.Module,
-        timeout_seconds: float,
-        on_timeout: TimeoutCallback | None,
-    ) -> nn.Module:
-        model = cast(GLM4MoeLiteModel, model)
-        for layer in model.layers:  # type: ignore
-            layer = cast(Glm4MoeLiteDecoderLayer, layer)
-            eval_with_timeout(
-                layer.parameters(),
-                timeout_seconds / len(model.layers),  # type: ignore
-                on_timeout,
-            )
-            if layer.self_attn.q_lora_rank is None:  # type: ignore
-                layer.self_attn.q_proj = self.all_to_sharded_linear(
-                    layer.self_attn.q_proj
-                )
-            else:
-                layer.self_attn.q_b_proj = self.all_to_sharded_linear(
-                    layer.self_attn.q_b_proj
-                )
-
-            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
-            layer.self_attn.num_heads //= self.N
-
-            # Logic from upstream mlx
-            num_heads = layer.self_attn.num_heads
-            sh = self.group.rank() * num_heads
-            eh = sh + num_heads
-
-            def shard_heads(w: mx.array, sh: int = sh, eh: int = eh) -> mx.array:
-                return w[sh:eh]
-
-            layer.self_attn.embed_q.apply(shard_heads)
-            layer.self_attn.unembed_out.apply(shard_heads)
-
-            if isinstance(layer.mlp, Glm4MoeLiteMLP):
-                layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
-                layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
-                layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
-
-            else:
-                if getattr(layer.mlp, "shared_experts", None) is not None:
-                    self.all_to_sharded_linear_in_place(
-                        layer.mlp.shared_experts.gate_proj
-                    )
-                    self.sharded_to_all_linear_in_place(
-                        layer.mlp.shared_experts.down_proj
-                    )
-                    self.all_to_sharded_linear_in_place(
-                        layer.mlp.shared_experts.up_proj
-                    )
-                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
-                self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
-                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
-                layer.mlp = ShardedGLM4MoeLiteMoE(layer.mlp)  # type: ignore
-                layer.mlp.sharding_group = self.group  # type: ignore
-            mx.eval(layer)
-
-        return model
-
-
-class ShardedGLM4MoeLiteMoE(CustomMlxLayer):
-    def __init__(self, layer: _LayerCallable):
-        super().__init__(layer)
-        self.sharding_group: mx.distributed.Group | None = None
-
-    def __call__(self, x: mx.array) -> mx.array:
-        if self.sharding_group is not None:
-            x = sum_gradients(self.sharding_group)(x)
-        y = self.original_layer.__call__(x)
-        if self.sharding_group is not None:
-            y = mx.distributed.all_sum(y, group=self.sharding_group)
-        return y
-
-
 class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
     def shard_model(
         self,
@@ -647,7 +566,7 @@ class MiniMaxShardingStrategy(TensorParallelShardingStrategy):
             )
             layer.block_sparse_moe = ShardedQwenMoE(layer.block_sparse_moe)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
             layer.block_sparse_moe.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
-            mx.eval(layer)
+
         return model
 
 
@@ -688,7 +607,6 @@ class QwenShardingStrategy(TensorParallelShardingStrategy):
                 layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
                 layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
 
-            mx.eval(layer)
         return model
 
 
@@ -743,7 +661,7 @@ class GptOssShardingStrategy(TensorParallelShardingStrategy):
 
             layer.mlp = ShardedGptOssMoE(layer.mlp)  # type: ignore
             layer.mlp.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
-            mx.eval(layer)
+
         return model
 
 

src/exo/worker/engines/mlx/cache.py

@@ -1,81 +1,39 @@
-import os
+# type: ignore
+# TODO: Fix this file, including types!
 from copy import deepcopy
-from typing import Any, cast
+from typing import Callable
 
 import mlx.core as mx
-from mlx_lm.models.cache import (
-    KVCache,
-    QuantizedKVCache,
-    RotatingKVCache,
-    trim_prompt_cache,
-)
-from mlx_lm.models.gpt_oss import Model as GptOssModel
+from mlx_lm import stream_generate
+from mlx_lm.models.cache import _BaseCache, trim_prompt_cache
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
-from exo.shared.types.mlx import KVCacheType
 from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.constants import CACHE_GROUP_SIZE, KV_CACHE_BITS
-from exo.worker.runner.bootstrap import logger
-
-# Fraction of device memory above which LRU eviction kicks in
-_DEFAULT_MEMORY_THRESHOLD = 0.85
-_MEMORY_THRESHOLD = float(
-    os.environ.get("EXO_MEMORY_THRESHOLD", _DEFAULT_MEMORY_THRESHOLD)
-)
+from exo.worker.engines.mlx.constants import KEEP_KV_SIZE, KV_BITS, KV_GROUP_SIZE
+from exo.worker.engines.mlx.utils_mlx import make_kv_cache
 
 
 class KVPrefixCache:
-    def __init__(self, tokenizer: TokenizerWrapper):
+    def __init__(self):
+        # Only one prefix cache per runner.
         self.prompts: list[mx.array] = []  # mx array of tokens (ints)
-        self.caches: list[KVCacheType] = []
-        self._last_used: list[int] = []  # monotonic counter of last access per entry
-        self._access_counter: int = 0
-        self._tokenizer: TokenizerWrapper = tokenizer
+        self.caches: list[list[_BaseCache]] = []
 
-    def clear(self):
-        """Clear all cached prompts and caches."""
-        self.prompts.clear()
-        self.caches.clear()
-        self._last_used.clear()
-
-    def add_kv_cache(self, prompt: str, cache: KVCacheType):
-        """Add a new cache entry. Evicts LRU entries if memory is high."""
-        self._evict_if_needed()
-        tokenized_prompt = encode_prompt(self._tokenizer, prompt)
+    def add_kv_cache(
+        self, tokenizer: TokenizerWrapper, prompt: str, cache: list[_BaseCache]
+    ):
+        tokenized_prompt = self.encode_prompt(tokenizer, prompt)
         self.prompts.append(tokenized_prompt)
         self.caches.append(deepcopy(cache))
-        self._access_counter += 1
-        self._last_used.append(self._access_counter)
-        logger.info(f"KV cache added: {len(tokenized_prompt)} tokens")
-
-    def update_kv_cache(
-        self,
-        index: int,
-        prompt: str,
-        cache: KVCacheType,
-    ):
-        """Update an existing cache entry in-place."""
-        tokenized_prompt = encode_prompt(self._tokenizer, prompt)
-        self.prompts[index] = tokenized_prompt
-        self.caches[index] = deepcopy(cache)
-        self._access_counter += 1
-        self._last_used[index] = self._access_counter
-        logger.info(f"KV cache updated (index {index}): {len(tokenized_prompt)} tokens")
 
     def get_kv_cache(
         self,
         model: Model,
+        tokenizer: TokenizerWrapper,
+        sampler: Callable[[mx.array], mx.array],
         prompt: str,
-    ) -> tuple[KVCacheType, mx.array, int | None]:
-        """Get KV cache for prompt, returning remaining tokens to prefill.
-
-        Returns:
-            Tuple of (cache, remaining_tokens, matched_index) where:
-            - cache: KV cache to use for generation
-            - remaining_tokens: tokens that still need prefilling
-            - matched_index: index of the matched entry (None if no match)
-        """
-        tokenized_prompt = encode_prompt(self._tokenizer, prompt)
+    ) -> list[_BaseCache]:
+        tokenized_prompt = self.encode_prompt(tokenizer, prompt)
         max_length = len(tokenized_prompt)
 
         best_snapshot_index, best_snapshot_length = None, 0
@@ -84,127 +42,63 @@ class KVPrefixCache:
             length = _get_prefix_length(tokenized_prompt, cached_prompt)
 
             if length == max_length:
-                # Exact match - cached prompt starts with our entire prompt
-                # Trim cache to prompt length - 1, return last token for stream_generate
-                prompt_cache = deepcopy(self.caches[i])
-                cached_length = _cache_length(self.caches[i])
-                tokens_to_trim = cached_length - (max_length - 1)
-                if tokens_to_trim > 0:
-                    trim_prompt_cache(cast(list[Any], prompt_cache), tokens_to_trim)
-                self._access_counter += 1
-                self._last_used[i] = self._access_counter
-                logger.info(f"KV cache exact match: {max_length} tokens (instant)")
-                return prompt_cache, tokenized_prompt[-1:], i
+                return self.caches[i]
 
             if length > best_snapshot_length:
                 best_snapshot_index, best_snapshot_length = i, length
 
         if best_snapshot_index is not None:
-            new_tokens = max_length - best_snapshot_length
-            logger.info(
-                f"KV cache prefix match: {best_snapshot_length}/{max_length} tokens "
-                f"(reusing {best_snapshot_length}, need to prefill {new_tokens})"
-            )
-
             prompt_cache = deepcopy(self.caches[best_snapshot_index])
-
-            # Trim removes tokens from the end, so we trim (cached_length - prefix_length) to keep the prefix
-            cached_length = _cache_length(self.caches[best_snapshot_index])
-            tokens_to_trim = cached_length - best_snapshot_length
-            if tokens_to_trim > 0:
-                trim_prompt_cache(cast(list[Any], prompt_cache), tokens_to_trim)
-
-            self._access_counter += 1
-            self._last_used[best_snapshot_index] = self._access_counter
-            remaining_tokens = tokenized_prompt[best_snapshot_length:]
-            return prompt_cache, remaining_tokens, best_snapshot_index
+            trim_prompt_cache(prompt_cache, max_length - best_snapshot_length)
+            tokenized_prompt = tokenized_prompt[best_snapshot_index:]
 
         else:
-            prompt_cache = make_kv_cache(model)
-            if len(self.prompts) == 0:
-                logger.info(f"KV cache empty, need to prefill {max_length} tokens")
-            else:
-                logger.info(
-                    f"KV cache no prefix match, need to prefill {max_length} tokens"
-                )
-
-            return prompt_cache, tokenized_prompt, None
-
-    def _evict_if_needed(self):
-        """Evict least recently used entries while memory pressure is high."""
-        if len(self.caches) == 0:
-            return
-
-        active: int = mx.metal.get_active_memory()
-        limit = int(mx.metal.device_info()["max_recommended_working_set_size"])
-        if active < limit * _MEMORY_THRESHOLD:
-            return
-
-        # Evict LRU entries until below threshold or only one entry left
-        while len(self.caches) > 0:
-            lru_index = self._last_used.index(min(self._last_used))
-            evicted_tokens = len(self.prompts[lru_index])
-            self.prompts.pop(lru_index)
-            self.caches.pop(lru_index)
-            self._last_used.pop(lru_index)
-            logger.info(
-                f"KV cache evicted LRU entry ({evicted_tokens} tokens) due to memory pressure"
+            prompt_cache = make_kv_cache(
+                model,
+                # max_kv_size=MAX_KV_SIZE,
+                # keep=KEEP_KV_SIZE
             )
 
-            active = mx.metal.get_active_memory()
-            if active < limit * _MEMORY_THRESHOLD:
-                break
+        prefill(model, tokenizer, sampler, tokenized_prompt, prompt_cache)
 
+        return prompt_cache
 
-def encode_prompt(tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
-    """Encode a prompt string to token array.
-
-    For chat-templated prompts (which have their own structure markers like
-    <|im_user|>, <|im_middle|>, etc.), we should NOT add BOS/EOS tokens as
-    that would corrupt the prompt structure.
-    """
-    # Chat templates define their own structure - don't add BOS/EOS
-    tokenized_prompt = tokenizer.encode(prompt, add_special_tokens=False)
-    return mx.array(tokenized_prompt)
-
-
-def _cache_length(cache: KVCacheType) -> int:
-    """Get the number of tokens in a KV cache."""
-    # Use .offset attribute which all cache types have (len() not implemented in older QuantizedKVCache)
-    return max(c.offset for c in cache)  # type: ignore
+    def encode_prompt(self, tokenizer: TokenizerWrapper, prompt: str) -> mx.array:
+        add_special_tokens = tokenizer.bos_token is None or not prompt.startswith(
+            tokenizer.bos_token
+        )
+        tokenized_prompt = tokenizer.encode(
+            prompt, add_special_tokens=add_special_tokens
+        )
+        return mx.array(tokenized_prompt)
 
 
 def _get_prefix_length(prompt: mx.array, cached_prompt: mx.array) -> int:
-    """Find the length of the common prefix between two token arrays."""
-    n = min(int(prompt.shape[0]), int(cached_prompt.shape[0]))
+    n = min(int(prompt.shape[0]), int(cached_prompt.shape[0]), KEEP_KV_SIZE)
     if n == 0:
         return 0
 
-    equal = mx.equal(prompt[:n], cached_prompt[:n]).astype(mx.int32)
+    equal = (prompt[:n] == cached_prompt[:n]).astype(mx.int32)
     prefix_mask = mx.cumprod(equal)  # stays 1 until first mismatch, then 0 forever
     return int(mx.sum(prefix_mask).item())
 
 
-def make_kv_cache(
-    model: Model, max_kv_size: int | None = None, keep: int = 0
-) -> KVCacheType:
-    assert hasattr(model, "layers")
-
-    # TODO: Do this for all models
-    if hasattr(model, "make_cache") and isinstance(model, GptOssModel):
-        logger.info("Using MLX LM's make cache")
-        return model.make_cache()  # type: ignore
-
-    if max_kv_size is None:
-        if KV_CACHE_BITS is None:
-            logger.info("Using default KV cache")
-            return [KVCache() for _ in model.layers]
-        else:
-            logger.info("Using quantized KV cache")
-            return [
-                QuantizedKVCache(group_size=CACHE_GROUP_SIZE, bits=KV_CACHE_BITS)
-                for _ in model.layers
-            ]
-    else:
-        logger.info(f"Using rotating KV cache with {max_kv_size=} with {keep=}")
-        return [RotatingKVCache(max_size=max_kv_size, keep=keep) for _ in model.layers]
+def prefill(
+    model: Model,
+    tokenizer: TokenizerWrapper,
+    sampler: Callable[[mx.array], mx.array],
+    prompt: mx.array,
+    cache: list[_BaseCache],
+) -> None:
+    for _ in stream_generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompt,
+        max_tokens=0,
+        sampler=sampler,
+        prompt_cache=cache,
+        prefill_step_size=2048,
+        kv_group_size=KV_GROUP_SIZE,
+        kv_bits=KV_BITS,
+    ):
+        pass

src/exo/worker/engines/mlx/constants.py

@@ -4,7 +4,7 @@
 KV_GROUP_SIZE: int | None = 32
 KV_BITS: int | None = None
 ATTENTION_KV_BITS: int | None = 4
-MAX_TOKENS: int = 32168
+MAX_TOKENS: int = 8192
 MAX_KV_SIZE: int | None = 3200
 KEEP_KV_SIZE: int | None = 1600
 QUANTIZE_MODEL_MODE: str | None = "affine"

src/exo/worker/engines/mlx/generator/generate.py

@@ -1,12 +1,12 @@
-import time
 from typing import Any, Callable, Generator, cast, get_args
 
 import mlx.core as mx
 from mlx_lm.generate import stream_generate
-from mlx_lm.models.cache import trim_prompt_cache
+from mlx_lm.models.cache import KVCache
 from mlx_lm.sample_utils import make_sampler
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
+# from exo.engines.mlx.cache import KVPrefixCache
 from exo.shared.types.api import (
     BenchChatCompletionTaskParams,
     ChatCompletionMessage,
@@ -14,78 +14,35 @@ from exo.shared.types.api import (
     GenerationStats,
 )
 from exo.shared.types.memory import Memory
-from exo.shared.types.mlx import KVCacheType
 from exo.shared.types.tasks import ChatCompletionTaskParams
 from exo.shared.types.worker.runner_response import (
     GenerationResponse,
 )
 from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.cache import KVPrefixCache, encode_prompt, make_kv_cache
 from exo.worker.engines.mlx.constants import KV_BITS, KV_GROUP_SIZE, MAX_TOKENS
 from exo.worker.engines.mlx.utils_mlx import (
     apply_chat_template,
+    make_kv_cache,
     mx_barrier,
 )
 from exo.worker.runner.bootstrap import logger
 
 generation_stream = mx.new_stream(mx.default_device())
 
-_MIN_PREFIX_HIT_TO_UPDATE = 1000
 
-
-def prefill(
-    model: Model,
-    tokenizer: TokenizerWrapper,
-    sampler: Callable[[mx.array], mx.array],
-    prompt_tokens: mx.array,
-    cache: KVCacheType,
-) -> float:
-    """Prefill the KV cache with prompt tokens.
-
-    This runs the model over the prompt tokens to populate the cache,
-    then trims off the extra generated token.
-
-    Returns:
-        tokens_per_sec
-    """
-    num_tokens = len(prompt_tokens)
-    if num_tokens == 0:
-        return 0.0
-
-    logger.debug(f"Prefilling {num_tokens} tokens...")
-    start_time = time.perf_counter()
-
-    def progress_callback(processed: int, total: int) -> None:
-        elapsed = time.time() - start_time
-        tok_per_sec = processed / elapsed if elapsed > 0 else 0
-        logger.debug(
-            f"Prefill progress: {processed}/{total} tokens ({tok_per_sec:.1f} tok/s)"
-        )
-
-    # Use max_tokens=1 because max_tokens=0 does not work.
-    # We just throw away the generated token - we only care about filling the cache
-    for _ in stream_generate(
-        model=model,
-        tokenizer=tokenizer,
-        prompt=prompt_tokens,
-        max_tokens=1,
-        sampler=sampler,
-        prompt_cache=cache,
-        prefill_step_size=2048,
-        kv_group_size=KV_GROUP_SIZE,
-        kv_bits=KV_BITS,
-        prompt_progress_callback=progress_callback,
-    ):
-        break  # Stop after first iteration - cache is now filled
-    trim_prompt_cache(cast(list[Any], cache), 1)
-
-    elapsed = time.perf_counter() - start_time
-    tokens_per_sec = num_tokens / elapsed if elapsed > 0 else 0.0
-    logger.debug(
-        f"Prefill complete: {num_tokens} tokens in {elapsed:.2f}s "
-        f"({tokens_per_sec:.1f} tok/s)"
-    )
-    return tokens_per_sec
+def maybe_quantize_kv_cache(
+    prompt_cache: list[KVCache | Any],
+    quantized_kv_start: int,
+    kv_group_size: int,
+    kv_bits: int | None,
+) -> None:
+    if kv_bits is None:
+        return
+    for e, c in enumerate(prompt_cache):
+        if (
+            hasattr(c, "to_quantized") and c.offset >= quantized_kv_start  # type: ignore
+        ):
+            prompt_cache[e] = c.to_quantized(group_size=kv_group_size, bits=kv_bits)
 
 
 def warmup_inference(
@@ -163,7 +120,6 @@ def mlx_generate(
     tokenizer: TokenizerWrapper,
     task: ChatCompletionTaskParams,
     prompt: str,
-    kv_prefix_cache: KVPrefixCache | None = None,
 ) -> Generator[GenerationResponse]:
     # Ensure that generation stats only contains peak memory for this generation
     mx.reset_peak_memory()
@@ -175,22 +131,7 @@ def mlx_generate(
     if task.seed is not None:
         mx.random.seed(task.seed)
 
-    # Do not use the prefix cache if we are trying to do benchmarks.
-    if is_bench:
-        kv_prefix_cache = None
-
-    # Use prefix cache if available, otherwise create fresh cache
-    prefix_hit_length = 0
-    matched_index: int | None = None
-    if kv_prefix_cache is None:
-        caches = make_kv_cache(model=model)
-        prompt_tokens = encode_prompt(tokenizer, prompt)
-    else:
-        caches, prompt_tokens, matched_index = kv_prefix_cache.get_kv_cache(
-            model, prompt
-        )
-        all_prompt_tokens = encode_prompt(tokenizer, prompt)
-        prefix_hit_length = len(all_prompt_tokens) - len(prompt_tokens)
+    caches = make_kv_cache(model=model)
 
     logits_processors: list[Callable[[mx.array, mx.array], mx.array]] = []
     if is_bench:
@@ -203,19 +144,11 @@ def mlx_generate(
         top_p=task.top_p if task.top_p is not None else 1.0,
     )
 
-    # Prefill cache with all tokens except the last one
-    prefill_tps = prefill(model, tokenizer, sampler, prompt_tokens[:-1], caches)
-
-    # stream_generate starts from the last token
-    last_token = prompt_tokens[-1:]
-
     max_tokens = task.max_tokens or MAX_TOKENS
-    generated_text_parts: list[str] = []
-    generation_start_time = time.perf_counter()
     for out in stream_generate(
         model=model,
         tokenizer=tokenizer,
-        prompt=last_token,
+        prompt=prompt,
         max_tokens=max_tokens,
         sampler=sampler,
         logits_processors=logits_processors,
@@ -225,13 +158,12 @@ def mlx_generate(
         kv_group_size=KV_GROUP_SIZE,
         kv_bits=KV_BITS,
     ):
-        generated_text_parts.append(out.text)
         logger.info(out.text)
 
         stats: GenerationStats | None = None
         if out.finish_reason is not None:
             stats = GenerationStats(
-                prompt_tps=float(prefill_tps or out.prompt_tps),
+                prompt_tps=float(out.prompt_tps),
                 generation_tps=float(out.generation_tps),
                 prompt_tokens=int(out.prompt_tokens),
                 generation_tokens=int(out.generation_tokens),
@@ -253,26 +185,6 @@ def mlx_generate(
         )
 
         if out.finish_reason is not None:
-            # Log generation stats
-            generation_elapsed = time.perf_counter() - generation_start_time
-            generated_tokens = len(generated_text_parts)
-            generation_tps = (
-                generated_tokens / generation_elapsed if generation_elapsed > 0 else 0.0
-            )
-            logger.debug(
-                f"Generation complete: prefill {prompt_tokens} tokens @ "
-                f"{prefill_tps:.1f} tok/s, generated {generated_tokens} tokens @ "
-                f"{generation_tps:.1f} tok/s"
-            )
-            if kv_prefix_cache is not None:
-                full_prompt = prompt + "".join(generated_text_parts)
-                if (
-                    matched_index is not None
-                    and prefix_hit_length >= _MIN_PREFIX_HIT_TO_UPDATE
-                ):
-                    kv_prefix_cache.update_kv_cache(matched_index, full_prompt, caches)
-                else:
-                    kv_prefix_cache.add_kv_cache(full_prompt, caches)
             break
 
         # TODO: Do we want an mx_barrier?

src/exo/worker/engines/mlx/utils_mlx.py

@@ -18,12 +18,15 @@ try:
 except ImportError:
     pass  # transformers < 5.0 or bytes_to_unicode not available
 
-from mlx_lm.models.cache import KVCache
+from mlx_lm.models.cache import KVCache, QuantizedKVCache, RotatingKVCache
 from mlx_lm.models.deepseek_v3 import DeepseekV3Model
+from mlx_lm.models.gpt_oss import Model as GptOssModel
 from mlx_lm.tokenizer_utils import TokenizerWrapper
 
 from exo.shared.models.model_cards import ModelId
 from exo.worker.engines.mlx.constants import (
+    CACHE_GROUP_SIZE,
+    KV_CACHE_BITS,
     TRUST_REMOTE_CODE,
 )
 
@@ -167,10 +170,10 @@ def mlx_distributed_init(
 
                 # TODO: update once upstream fixes
                 logger.info(
-                    f"rank {rank} MLX_IBV_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
+                    f"rank {rank} MLX_JACCL_DEVICES: {coordination_file} with devices: {jaccl_devices_json}"
                 )
                 logger.info(f"rank {rank} MLX_JACCL_COORDINATOR: {jaccl_coordinator}")
-                os.environ["MLX_IBV_DEVICES"] = coordination_file
+                os.environ["MLX_JACCL_DEVICES"] = coordination_file
                 os.environ["MLX_RANK"] = str(rank)
                 os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator
                 group = mx.distributed.init(backend="jaccl", strict=True)
@@ -402,11 +405,7 @@ def apply_chat_template(
                 continue
 
             message.content = "\n".join(c.text for c in message.content).strip()
-        if (
-            message.content is None
-            and message.thinking is None
-            and message.tool_calls is None
-        ):
+        if message.content is None and message.thinking is None:
             continue
 
         # Null values are not valid when applying templates in tokenizer
@@ -463,6 +462,31 @@ class NullKVCache(KVCache):
         raise NotImplementedError("We should not be setting a NullKVCache.")
 
 
+def make_kv_cache(
+    model: Model, max_kv_size: int | None = None, keep: int = 0
+) -> list[KVCache | RotatingKVCache | QuantizedKVCache]:
+    assert hasattr(model, "layers")
+
+    # TODO: Do this for all models
+    if hasattr(model, "make_cache") and isinstance(model, GptOssModel):
+        logger.info("Using MLX LM's make cache")
+        return model.make_cache()  # type: ignore
+
+    if max_kv_size is None:
+        if KV_CACHE_BITS is None:
+            logger.info("Using default KV cache")
+            return [KVCache() for _ in model.layers]
+        else:
+            logger.info("Using quantized KV cache")
+            return [
+                QuantizedKVCache(group_size=CACHE_GROUP_SIZE, bits=KV_CACHE_BITS)
+                for _ in model.layers
+            ]
+    else:
+        logger.info(f"Using rotating KV cache with {max_kv_size=} with {keep=}")
+        return [RotatingKVCache(max_size=max_kv_size, keep=keep) for _ in model.layers]
+
+
 def mlx_force_oom(size: int = 40000) -> None:
     """
     Force an Out-Of-Memory (OOM) error in MLX by performing large tensor operations.

src/exo/worker/runner/runner.py

@@ -70,7 +70,6 @@ from exo.worker.engines.image import (
     warmup_image_generator,
 )
 from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.cache import KVPrefixCache
 from exo.worker.engines.mlx.generator.generate import mlx_generate, warmup_inference
 from exo.worker.engines.mlx.utils_mlx import (
     apply_chat_template,
@@ -104,7 +103,6 @@ def main(
     model: Model | DistributedImageModel | None = None
     tokenizer = None
     group = None
-    kv_prefix_cache: KVPrefixCache | None = None
 
     current_status: RunnerStatus = RunnerIdle()
     logger.info("runner created")
@@ -163,8 +161,6 @@ def main(
                         logger.info(
                             f"model has_tool_calling={tokenizer.has_tool_calling}"
                         )
-                        kv_prefix_cache = KVPrefixCache(tokenizer)
-
                     elif (
                         ModelTask.TextToImage in shard_metadata.model_card.tasks
                         or ModelTask.ImageToImage in shard_metadata.model_card.tasks
@@ -174,6 +170,7 @@ def main(
                         raise ValueError(
                             f"Unknown model task(s): {shard_metadata.model_card.tasks}"
                         )
+
                     current_status = RunnerLoaded()
                     logger.info("runner loaded")
                 case StartWarmup() if isinstance(current_status, RunnerLoaded):
@@ -241,7 +238,6 @@ def main(
                             tokenizer=tokenizer,
                             task=task_params,
                             prompt=prompt,
-                            kv_prefix_cache=kv_prefix_cache,
                         )
 
                         # For other thinking models (GLM, etc.), check if we need to

src/exo/worker/tests/unittests/test_mlx/test_kv_prefix_cache.py

@@ -1,545 +0,0 @@
-# type: ignore
-import time
-from typing import cast
-from unittest.mock import patch
-
-import mlx.core as mx
-import pytest
-from mlx_lm.models.cache import KVCache
-from mlx_lm.sample_utils import make_sampler
-
-from exo.shared.types.api import ChatCompletionMessage
-from exo.shared.types.common import ModelId
-from exo.shared.types.tasks import ChatCompletionTaskParams
-from exo.worker.engines.mlx import Model
-from exo.worker.engines.mlx.cache import (
-    KVPrefixCache,
-    _cache_length,
-    _get_prefix_length,
-    encode_prompt,
-    make_kv_cache,
-)
-from exo.worker.engines.mlx.generator.generate import mlx_generate, prefill
-from exo.worker.engines.mlx.utils_mlx import apply_chat_template
-from exo.worker.tests.unittests.test_mlx.conftest import (
-    DEFAULT_GPT_OSS_CONFIG,
-    DEFAULT_GPT_OSS_MODEL_ID,
-)
-
-
-def _check_model_exists() -> bool:
-    return DEFAULT_GPT_OSS_CONFIG.model_path.exists()
-
-
-class TestGetPrefixLength:
-    def test_identical_arrays(self):
-        a = mx.array([1, 2, 3, 4, 5])
-        b = mx.array([1, 2, 3, 4, 5])
-        assert _get_prefix_length(a, b) == 5
-
-    def test_no_common_prefix(self):
-        a = mx.array([1, 2, 3])
-        b = mx.array([4, 5, 6])
-        assert _get_prefix_length(a, b) == 0
-
-    def test_partial_prefix(self):
-        a = mx.array([1, 2, 3, 4, 5])
-        b = mx.array([1, 2, 3, 7, 8])
-        assert _get_prefix_length(a, b) == 3
-
-    def test_prompt_longer_than_cached(self):
-        a = mx.array([1, 2, 3, 4, 5])
-        b = mx.array([1, 2, 3])
-        assert _get_prefix_length(a, b) == 3
-
-    def test_cached_longer_than_prompt(self):
-        a = mx.array([1, 2, 3])
-        b = mx.array([1, 2, 3, 4, 5])
-        assert _get_prefix_length(a, b) == 3
-
-    def test_single_token_match(self):
-        a = mx.array([1, 2, 3])
-        b = mx.array([1, 5, 6])
-        assert _get_prefix_length(a, b) == 1
-
-    def test_empty_prompt(self):
-        a = mx.array([]).astype(mx.int32)
-        b = mx.array([1, 2, 3])
-        assert _get_prefix_length(a, b) == 0
-
-    def test_empty_cached(self):
-        a = mx.array([1, 2, 3])
-        b = mx.array([]).astype(mx.int32)
-        assert _get_prefix_length(a, b) == 0
-
-    def test_both_empty(self):
-        a = mx.array([]).astype(mx.int32)
-        b = mx.array([]).astype(mx.int32)
-        assert _get_prefix_length(a, b) == 0
-
-
-class TestKVPrefix:
-    @pytest.fixture
-    def mock_tokenizer(self):
-        """Create a minimal mock tokenizer for tests that don't need real tokenization."""
-        from unittest.mock import MagicMock
-
-        tokenizer = MagicMock()
-        tokenizer.encode.return_value = [1, 2, 3]
-        return tokenizer
-
-    def test_starts_empty(self, mock_tokenizer):
-        cache = KVPrefixCache(mock_tokenizer)
-        assert len(cache.prompts) == 0
-        assert len(cache.caches) == 0
-
-    def test_clear_empties_cache(self, mock_tokenizer):
-        cache = KVPrefixCache(mock_tokenizer)
-        cache.prompts.append(mx.array([1, 2, 3]))
-        cache.caches.append([KVCache()])
-        cache.clear()
-        assert len(cache.prompts) == 0
-        assert len(cache.caches) == 0
-
-    def test_clear_on_empty_cache(self, mock_tokenizer):
-        cache = KVPrefixCache(mock_tokenizer)
-        cache.clear()
-        assert len(cache.prompts) == 0
-
-
-def _load_gpt_oss() -> tuple[Model, object]:
-    from mlx_lm.utils import load_model
-
-    from exo.worker.engines.mlx.utils_mlx import load_tokenizer_for_model_id
-
-    model_path = DEFAULT_GPT_OSS_CONFIG.model_path
-    model_id = ModelId(DEFAULT_GPT_OSS_MODEL_ID)
-
-    model, _ = load_model(model_path, lazy=False)
-    tokenizer = load_tokenizer_for_model_id(model_id, model_path)
-    return cast(Model, model), tokenizer
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    not _check_model_exists(),
-    reason=f"GPT-OSS model not found at {DEFAULT_GPT_OSS_CONFIG.model_path}",
-)
-class TestKVPrefixCacheWithModel:
-    @pytest.fixture(scope="class")
-    def model_and_tokenizer(self):
-        model, tokenizer = _load_gpt_oss()
-        return model, tokenizer
-
-    def test_prefill_populates_cache(self, model_and_tokenizer):
-        model, tokenizer = model_and_tokenizer
-
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Hello!!")],
-            max_tokens=1,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        tokens = encode_prompt(tokenizer, prompt)
-        cache = make_kv_cache(model)
-
-        prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-
-        # Cache should now hold the prompt tokens
-        assert _cache_length(cache) == len(tokens)
-
-    def test_add_and_get_exact_match(self, model_and_tokenizer):
-        model, tokenizer = model_and_tokenizer
-
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Test exact")],
-            max_tokens=1,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        tokens = encode_prompt(tokenizer, prompt)
-        cache = make_kv_cache(model)
-
-        prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        kv_prefix_cache.add_kv_cache(prompt, cache)
-
-        assert len(kv_prefix_cache.prompts) == 1
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
-        assert stored_length > 0
-
-        # Retrieve with same prompt: exact match
-        result_cache, remaining_tokens, matched_index = kv_prefix_cache.get_kv_cache(
-            model, prompt
-        )
-        assert matched_index == 0
-
-        # Exact match returns only last token
-        assert len(remaining_tokens) == 1
-        assert mx.array_equal(remaining_tokens, tokens[-1:])
-
-    def test_add_and_get_prefix_match(self, model_and_tokenizer):
-        """get_kv_cache with a longer prompt sharing prefix should return partial match."""
-        model, tokenizer = model_and_tokenizer
-
-        short_task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Hi")],
-            max_tokens=1,
-        )
-        short_prompt = apply_chat_template(tokenizer, short_task)
-        short_tokens = encode_prompt(tokenizer, short_prompt)
-        cache = make_kv_cache(model)
-
-        prefill(model, tokenizer, make_sampler(0.0), short_tokens, cache)
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        kv_prefix_cache.add_kv_cache(short_prompt, cache)
-
-        # Query with longer prompt that shares the chat template prefix
-        long_task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[
-                ChatCompletionMessage(role="user", content="Hi there, how are you?")
-            ],
-            max_tokens=1,
-        )
-        long_prompt = apply_chat_template(tokenizer, long_task)
-        long_tokens = encode_prompt(tokenizer, long_prompt)
-
-        # The prompts share a prefix (chat template preamble + "Hi")
-        expected_prefix = _get_prefix_length(long_tokens, short_tokens)
-        assert expected_prefix > 0, (
-            "Prompts should share a prefix from the chat template"
-        )
-
-        result_cache, remaining_tokens, matched_index = kv_prefix_cache.get_kv_cache(
-            model, long_prompt
-        )
-        assert matched_index == 0
-
-        # remaining_tokens should be the suffix after the shared prefix
-        assert len(remaining_tokens) == len(long_tokens) - expected_prefix
-        assert mx.array_equal(remaining_tokens, long_tokens[expected_prefix:])
-
-    def test_stored_cache_not_mutated_after_get_and_generation(
-        self, model_and_tokenizer
-    ):
-        """Getting a cache and then mutating it (as generation does) must not corrupt stored cache."""
-        model, tokenizer = model_and_tokenizer
-
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Mutation test")],
-            max_tokens=1,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        tokens = encode_prompt(tokenizer, prompt)
-        cache = make_kv_cache(model)
-
-        prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        kv_prefix_cache.add_kv_cache(prompt, cache)
-
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
-
-        # Get cache and mutate it (simulating what generation does)
-        result_cache, _, matched_index = kv_prefix_cache.get_kv_cache(model, prompt)
-        assert matched_index == 0
-
-        # Simulate generation: feed many additional tokens through the cache
-        head_dim = result_cache[0].keys.shape[-1]
-        num_heads = result_cache[0].keys.shape[1]
-        extra_keys = mx.random.normal((1, num_heads, 50, head_dim))
-        extra_values = mx.random.normal((1, num_heads, 50, head_dim))
-        for layer_cache in result_cache:
-            layer_cache.update_and_fetch(extra_keys, extra_values)
-        mx.eval([c.keys for c in result_cache])
-
-        # Stored cache must be unchanged
-        assert _cache_length(kv_prefix_cache.caches[0]) == stored_length
-
-    def test_stored_cache_survives_repeated_get_mutate_cycles(
-        self, model_and_tokenizer
-    ):
-        """Multiple get+mutate cycles (like repeated user requests) must not corrupt cache."""
-        model, tokenizer = model_and_tokenizer
-
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Repeat test")],
-            max_tokens=1,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        tokens = encode_prompt(tokenizer, prompt)
-        cache = make_kv_cache(model)
-
-        prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        kv_prefix_cache.add_kv_cache(prompt, cache)
-
-        stored_length = _cache_length(kv_prefix_cache.caches[0])
-
-        for i in range(3):
-            result_cache, _, _ = kv_prefix_cache.get_kv_cache(model, prompt)
-
-            head_dim = result_cache[0].keys.shape[-1]
-            num_heads = result_cache[0].keys.shape[1]
-            extra = mx.random.normal((1, num_heads, 30, head_dim))
-            for layer_cache in result_cache:
-                layer_cache.update_and_fetch(extra, extra)
-            mx.eval([c.keys for c in result_cache])
-
-            assert _cache_length(kv_prefix_cache.caches[0]) == stored_length, (
-                f"Failed on loop {i}"
-            )
-
-    def test_mlx_generate_populates_cache(self, model_and_tokenizer):
-        """mlx_generate should save the cache after generation completes."""
-        model, tokenizer = model_and_tokenizer
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Hello")],
-            max_tokens=5,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        prompt_tokens = encode_prompt(tokenizer, prompt)
-
-        # Consume the entire generator so the cache-saving code after yield runs
-        generated_tokens = 0
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task,
-            prompt=prompt,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            generated_tokens += 1
-
-        assert len(kv_prefix_cache.prompts) == 1
-        assert len(kv_prefix_cache.caches) == 1
-        # Cache should contain prompt + generated tokens
-        expected_length = len(prompt_tokens) + generated_tokens
-        assert _cache_length(kv_prefix_cache.caches[0]) == expected_length
-
-    def test_mlx_generate_second_call_gets_prefix_hit(self, model_and_tokenizer):
-        """Second mlx_generate call with same prompt should get a prefix hit from stored cache."""
-        model, tokenizer = model_and_tokenizer
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Reuse test")],
-            max_tokens=5,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-        prompt_tokens = encode_prompt(tokenizer, prompt)
-
-        # First generation populates cache
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task,
-            prompt=prompt,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            pass
-
-        assert len(kv_prefix_cache.prompts) == 1
-
-        # Second call should find a prefix match (the stored cache contains
-        # prompt + generated tokens, which shares the prompt prefix)
-        result_cache, remaining_tokens, matched_index = kv_prefix_cache.get_kv_cache(
-            model, prompt
-        )
-        # The stored cache is longer than the prompt (it includes generated tokens),
-        # so this is a prefix match where our prompt is fully contained
-        assert matched_index == 0
-        # Exact match: remaining_tokens is just the last token
-        assert len(remaining_tokens) == 1
-        assert mx.array_equal(remaining_tokens, prompt_tokens[-1:])
-
-    def test_mlx_generate_long_prompt_updates_cache_in_place(self, model_and_tokenizer):
-        """With a prompt > 1000 tokens, second generation should update the cache entry in-place."""
-        model, tokenizer = model_and_tokenizer
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-
-        # Build a long user message (> 1000 tokens) to exceed _MIN_PREFIX_HIT_TO_UPDATE
-        base_text = "The quick brown fox jumps over the lazy dog. "
-        base_tokens = tokenizer.encode(base_text)
-        repeats = (1200 // len(base_tokens)) + 2
-        long_content = base_text * repeats
-
-        task1 = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content=long_content)],
-            max_tokens=5,
-        )
-        prompt1 = apply_chat_template(tokenizer, task1)
-        prompt1_tokens = encode_prompt(tokenizer, prompt1)
-        assert len(prompt1_tokens) > 1000, (
-            "Prompt must exceed _MIN_PREFIX_HIT_TO_UPDATE"
-        )
-
-        # First generation populates the cache (must prefill all tokens)
-        t0 = time.perf_counter()
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task1,
-            prompt=prompt1,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            pass
-        first_gen_time = time.perf_counter() - t0
-
-        assert len(kv_prefix_cache.prompts) == 1
-        first_cache_length = _cache_length(kv_prefix_cache.caches[0])
-
-        # Second generation: same long prompt + extra content (simulating multi-turn)
-        task2 = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[
-                ChatCompletionMessage(role="user", content=long_content),
-                ChatCompletionMessage(role="assistant", content="Sure, I can help."),
-                ChatCompletionMessage(role="user", content="Tell me more."),
-            ],
-            max_tokens=5,
-        )
-        prompt2 = apply_chat_template(tokenizer, task2)
-        prompt2_tokens = encode_prompt(tokenizer, prompt2)
-
-        # Verify the prompts share a long prefix
-        prefix_len = _get_prefix_length(prompt2_tokens, prompt1_tokens)
-        assert prefix_len > 1000, "Prompts must share > 1000 token prefix"
-
-        # Second generation should reuse the cached prefix (only prefill new tokens)
-        t0 = time.perf_counter()
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task2,
-            prompt=prompt2,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            pass
-        second_gen_time = time.perf_counter() - t0
-
-        # Second generation should be significantly faster due to prefix cache hit - hopefully not flaky
-        assert second_gen_time < first_gen_time * 0.5, (
-            f"Expected prefix cache speedup: "
-            f"first={first_gen_time:.2f}s, second={second_gen_time:.2f}s"
-        )
-
-        # With prefix_hit > 1000, should update in-place (not add a second entry)
-        assert len(kv_prefix_cache.prompts) == 1
-        # Updated cache should be longer (prompt2 + generated > prompt1 + generated)
-        updated_cache_length = _cache_length(kv_prefix_cache.caches[0])
-        assert updated_cache_length > first_cache_length
-
-    def test_mlx_generate_stored_cache_not_mutated(self, model_and_tokenizer):
-        """After mlx_generate saves a cache, a second generation must not corrupt the stored copy."""
-        model, tokenizer = model_and_tokenizer
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-        task = ChatCompletionTaskParams(
-            model=DEFAULT_GPT_OSS_MODEL_ID,
-            messages=[ChatCompletionMessage(role="user", content="Immutable test")],
-            max_tokens=5,
-        )
-        prompt = apply_chat_template(tokenizer, task)
-
-        # First generation populates cache
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task,
-            prompt=prompt,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            pass
-
-        first_cache_length = _cache_length(kv_prefix_cache.caches[0])
-
-        # Second generation gets the cache and mutates it during generation
-        for _response in mlx_generate(
-            model=model,
-            tokenizer=tokenizer,
-            task=task,
-            prompt=prompt,
-            kv_prefix_cache=kv_prefix_cache,
-        ):
-            pass
-
-        # The first stored cache must not have been mutated by the second generation
-        assert _cache_length(kv_prefix_cache.caches[0]) == first_cache_length
-
-    def test_evicts_lru_entry_under_memory_pressure(self, model_and_tokenizer):
-        """Under memory pressure, adding a new cache entry evicts the least recently used one."""
-        model, tokenizer = model_and_tokenizer
-
-        kv_prefix_cache = KVPrefixCache(tokenizer)
-
-        # Add three cache entries with different prompts
-        prompts = ["First entry", "Second entry", "Third entry"]
-        for i, content in enumerate(prompts):
-            task = ChatCompletionTaskParams(
-                model=DEFAULT_GPT_OSS_MODEL_ID,
-                messages=[ChatCompletionMessage(role="user", content=content)],
-                max_tokens=1,
-            )
-            prompt = apply_chat_template(tokenizer, task)
-            tokens = encode_prompt(tokenizer, prompt)
-            cache = make_kv_cache(model)
-            prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-            kv_prefix_cache.add_kv_cache(prompt, cache)
-            # Stagger _last_used so LRU order is deterministic
-            kv_prefix_cache._last_used[i] = float(i)
-
-        assert len(kv_prefix_cache.prompts) == 3
-
-        # Access the third entry to make it most recently used
-        kv_prefix_cache._last_used[2] = 100.0
-        # Entry 0 (_last_used=0.0) is LRU, entry 1 (_last_used=1.0) is next
-
-        # Simulate memory pressure: active memory exceeds threshold
-        fake_limit = 1000
-        fake_active = int(fake_limit * 0.90)  # Above _MEMORY_THRESHOLD (0.85)
-
-        with (
-            patch(
-                "exo.worker.engines.mlx.cache.mx.metal.get_active_memory",
-                return_value=fake_active,
-            ),
-            patch(
-                "exo.worker.engines.mlx.cache.mx.metal.device_info",
-                return_value={"max_recommended_working_set_size": fake_limit},
-            ),
-        ):
-            # Trigger eviction by adding a new entry
-            task = ChatCompletionTaskParams(
-                model=DEFAULT_GPT_OSS_MODEL_ID,
-                messages=[ChatCompletionMessage(role="user", content="New entry")],
-                max_tokens=1,
-            )
-            prompt = apply_chat_template(tokenizer, task)
-            tokens = encode_prompt(tokenizer, prompt)
-            cache = make_kv_cache(model)
-            prefill(model, tokenizer, make_sampler(0.0), tokens, cache)
-            kv_prefix_cache.add_kv_cache(prompt, cache)
-
-        # LRU entries should have been evicted (entries 0, 1, 2 in order of _last_used)
-        # Since fake_active stays above threshold after each eviction (we don't change it),
-        # all old entries get evicted, leaving only the newly added one
-        assert len(kv_prefix_cache.prompts) == 1
-        # The surviving entry should be the newly added one
-        new_tokens = encode_prompt(tokenizer, prompt)
-        assert _get_prefix_length(kv_prefix_cache.prompts[0], new_tokens) == len(
-            new_tokens
-        )

uv.lock

@@ -376,8 +376,8 @@ dependencies = [
     { name = "hypercorn", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mflux", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.3", source = { registry = "https://pypi.org/simple" }, extra = ["cpu"], marker = "sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.4.dev20260121+fbe306f9", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "mlx", extra = ["cpu"], marker = "sys_platform == 'linux'" },
     { name = "mlx-lm", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "openai-harmony", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "pillow", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -412,10 +412,10 @@ requires-dist = [
     { name = "huggingface-hub", specifier = ">=0.33.4" },
     { name = "hypercorn", specifier = ">=0.18.0" },
     { name = "loguru", specifier = ">=0.7.3" },
-    { name = "mlx", marker = "sys_platform == 'darwin'", git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git" },
     { name = "mflux", specifier = "==0.15.4" },
+    { name = "mlx", marker = "sys_platform == 'darwin'", specifier = "==0.30.3" },
     { name = "mlx", extras = ["cpu"], marker = "sys_platform == 'linux'", specifier = "==0.30.3" },
-    { name = "mlx-lm", specifier = "==0.30.5" },
+    { name = "mlx-lm", git = "https://github.com/AlexCheema/mlx-lm.git?rev=fix-transformers-5.0.0rc2" },
     { name = "openai-harmony", specifier = ">=0.0.8" },
     { name = "pillow", specifier = ">=11.0,<12.0" },
     { name = "psutil", specifier = ">=7.0.0" },
@@ -994,8 +994,8 @@ dependencies = [
     { name = "fonttools", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "huggingface-hub", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "matplotlib", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.3", source = { registry = "https://pypi.org/simple" }, extra = ["cuda13"], marker = "sys_platform == 'linux'" },
-    { name = "mlx", version = "0.30.4.dev20260121+fbe306f9", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "mlx", extra = ["cuda13"], marker = "sys_platform == 'linux'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "opencv-python", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "piexif", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -1022,12 +1022,18 @@ wheels = [
 name = "mlx"
 version = "0.30.3"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "sys_platform == 'linux'",
+dependencies = [
+    { name = "mlx-metal", marker = "sys_platform == 'darwin'" },
 ]
 wheels = [
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 ]
@@ -1040,14 +1046,6 @@ cuda13 = [
     { name = "mlx-cuda-13", marker = "sys_platform == 'linux'" },
 ]
 
-[[package]]
-name = "mlx"
-version = "0.30.4.dev20260121+fbe306f9"
-source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }
-resolution-markers = [
-    "sys_platform == 'darwin'",
-]
-
 [[package]]
 name = "mlx-cpu"
 version = "0.30.3"
@@ -1074,20 +1072,26 @@ wheels = [
 
 [[package]]
 name = "mlx-lm"
-version = "0.30.5"
-source = { registry = "https://pypi.org/simple" }
+version = "0.30.4"
+source = { git = "https://github.com/AlexCheema/mlx-lm.git?rev=fix-transformers-5.0.0rc2#a5daf2b894f31793dfaef0fdf9bc3ed683176ad6" }
 dependencies = [
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-    { name = "mlx", version = "0.30.4.dev20260121+fbe306f9", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git#fbe306f92a47d9b887ee7af2e3af6f1b9e28e663" }, marker = "sys_platform == 'darwin'" },
+    { name = "mlx", marker = "sys_platform == 'darwin'" },
     { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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     { name = "sentencepiece", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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+
+[[package]]
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+    { url = "https://files.pythonhosted.org/packages/fe/36/7a3cbca85542b5ca4faf871e35927f43aa0e3fc830ae5b699780fe723677/mlx_metal-0.30.3-py3-none-macosx_26_0_arm64.whl", hash = "sha256:69068533bd1ee8b0379ce5de57ed5fd313577a10ecab58e1332fd1ff7248a75e", size = 46488962, upload-time = "2026-01-14T05:52:04.523Z" },
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 [[package]]
@@ -2277,7 +2281,7 @@ wheels = [
 
 [[package]]
 name = "transformers"
-version = "5.0.0rc3"
+version = "5.0.0rc2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -2292,9 +2296,9 @@ dependencies = [
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     { name = "typer-slim", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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 [[package]]