x/imagegen: respect OLLAMA_MODELS for manifests and blobs (#13797)

Co-Authored-By: TommyBoiss <165361500+TommyBoiss@users.noreply.github.com>

api/types.go

@@ -749,7 +749,7 @@ type ShowResponse struct {
 	Messages      []Message          `json:"messages,omitempty"`
 	RemoteModel   string             `json:"remote_model,omitempty"`
 	RemoteHost    string             `json:"remote_host,omitempty"`
-	ModelInfo     map[string]any     `json:"model_info,omitempty"`
+	ModelInfo     map[string]any     `json:"model_info"`
 	ProjectorInfo map[string]any     `json:"projector_info,omitempty"`
 	Tensors       []Tensor           `json:"tensors,omitempty"`
 	Capabilities  []model.Capability `json:"capabilities,omitempty"`

convert/convert.go

@@ -313,6 +313,8 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &deepseek2Model{}
 	case "Glm4MoeLiteForCausalLM":
 		conv = &glm4MoeLiteModel{}
+	case "Lfm2ForCausalLM":
+		conv = &lfm2Model{}
 	default:
 		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}

convert/convert_lfm2.go

@@ -0,0 +1,100 @@
+package convert
+
+import (
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type lfm2Model struct {
+	ModelParameters
+	HiddenSize            uint32   `json:"hidden_size"`
+	NumHiddenLayers       uint32   `json:"num_hidden_layers"`
+	MaxPositionEmbeddings uint32   `json:"max_position_embeddings"`
+	IntermediateSize      uint32   `json:"intermediate_size"`
+	NumAttentionHeads     uint32   `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32   `json:"num_key_value_heads"`
+	RopeTheta             float32  `json:"rope_theta"`
+	NormEps               float32  `json:"norm_eps"`
+	ConvLCache            uint32   `json:"conv_L_cache"`
+	LayerTypes            []string `json:"layer_types"`
+	TieEmbedding          bool     `json:"tie_embedding"`
+}
+
+var _ ModelConverter = (*lfm2Model)(nil)
+
+func (p *lfm2Model) KV(t *Tokenizer) KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "lfm2"
+	kv["lfm2.vocab_size"] = p.VocabSize
+	kv["lfm2.block_count"] = p.NumHiddenLayers
+	kv["lfm2.embedding_length"] = p.HiddenSize
+	kv["lfm2.feed_forward_length"] = p.IntermediateSize
+	kv["lfm2.context_length"] = p.MaxPositionEmbeddings
+
+	// Build per-layer KV head count array based on layer_types
+	// (0 = shortconv layer, non-zero = attention layer with that many KV heads)
+	kvHeadCounts := make([]uint32, p.NumHiddenLayers)
+	for i := range p.NumHiddenLayers {
+		if int(i) < len(p.LayerTypes) && p.LayerTypes[i] == "full_attention" {
+			kvHeadCounts[i] = p.NumKeyValueHeads
+		}
+	}
+
+	kv["lfm2.attention.head_count"] = p.NumAttentionHeads
+	kv["lfm2.attention.head_count_kv"] = kvHeadCounts
+	kv["lfm2.attention.key_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.value_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.layer_norm_rms_epsilon"] = p.NormEps
+	kv["lfm2.rope.freq_base"] = p.RopeTheta
+	kv["lfm2.shortconv.l_cache"] = p.ConvLCache
+
+	return kv
+}
+
+func (p *lfm2Model) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+
+	for _, t := range ts {
+		shape := t.Shape()
+
+		// Squeeze conv weights: [D, 1, K] -> [D, K]
+		if strings.HasSuffix(t.Name(), "shortconv.conv.weight") {
+			if len(shape) == 3 && shape[1] == 1 {
+				shape = []uint64{shape[0], shape[2]}
+			}
+		}
+
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    slices.Clone(shape),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *lfm2Model) Replacements() []string {
+	return []string{
+		"model.embed_tokens", "token_embd",
+		"model.embedding_norm", "output_norm",
+		"model.layers", "blk",
+		"operator_norm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.out_proj", "attn_output",
+		"self_attn.q_layernorm", "attn_q_norm",
+		"self_attn.k_layernorm", "attn_k_norm",
+		"conv.conv", "shortconv.conv",
+		"conv.in_proj", "shortconv.in_proj",
+		"conv.out_proj", "shortconv.out_proj",
+		"feed_forward.w1", "ffn_gate",
+		"feed_forward.w2", "ffn_down",
+		"feed_forward.w3", "ffn_up",
+		"ffn_norm", "ffn_norm",
+	}
+}

convert/reader.go

@@ -40,6 +40,7 @@ const (
 func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
 		strings.HasSuffix(t.name, ".bias") ||
+		strings.HasSuffix(t.name, ".shortconv.conv.weight") ||
 		t.name == "token_types.weight" ||
 		t.name == "v.positional_embedding_vlm" ||
 		t.name == "v.tile_position_embd.weight" ||

fs/ggml/ggml.go

@@ -270,6 +270,7 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
 		"glm4moelite",
+		"lfm2",
 	}, kv.Architecture())
 }
 
@@ -859,6 +860,7 @@ func (f GGML) FlashAttention() bool {
 		"gemma3",
 		"glm4moelite",
 		"gptoss", "gpt-oss",
+		"lfm2",
 		"mistral3",
 		"olmo3",
 		"qwen3", "qwen3moe",

integration/imagegen_test.go

@@ -0,0 +1,148 @@
+//go:build integration
+
+package integration
+
+import (
+	"context"
+	"encoding/base64"
+	"fmt"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestImageGeneration(t *testing.T) {
+	skipUnderMinVRAM(t, 8)
+
+	type testCase struct {
+		imageGenModel string
+		visionModel   string
+		prompt        string
+		expectedWords []string
+	}
+
+	testCases := []testCase{
+		{
+			imageGenModel: "jmorgan/z-image-turbo",
+			visionModel:   "llama3.2-vision",
+			prompt:        "A cartoon style llama flying like a superhero through the air with clouds in the background",
+			expectedWords: []string{"llama", "flying", "cartoon", "cloud", "sky", "superhero", "air", "animal", "camelid"},
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(fmt.Sprintf("%s->%s", tc.imageGenModel, tc.visionModel), func(t *testing.T) {
+			ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+			defer cancel()
+
+			client, _, cleanup := InitServerConnection(ctx, t)
+			defer cleanup()
+
+			// Pull both models
+			if err := PullIfMissing(ctx, client, tc.imageGenModel); err != nil {
+				t.Fatalf("failed to pull image gen model: %v", err)
+			}
+			if err := PullIfMissing(ctx, client, tc.visionModel); err != nil {
+				t.Fatalf("failed to pull vision model: %v", err)
+			}
+
+			// Generate the image
+			t.Logf("Generating image with prompt: %s", tc.prompt)
+			imageBase64, err := generateImage(ctx, client, tc.imageGenModel, tc.prompt)
+			if err != nil {
+				if strings.Contains(err.Error(), "image generation not available") {
+					t.Skip("Target system does not support image generation")
+				} else if strings.Contains(err.Error(), "executable file not found in") { // Windows pattern, not yet supported
+					t.Skip("Windows does not support image generation yet")
+				} else if strings.Contains(err.Error(), "CUDA driver version is insufficient") {
+					t.Skip("Driver is too old")
+				} else if strings.Contains(err.Error(), "insufficient memory for image generation") {
+					t.Skip("insufficient memory for image generation")
+				} else if strings.Contains(err.Error(), "error while loading shared libraries: libcuda.so.1") { // AMD GPU or CPU
+					t.Skip("CUDA GPU is not available")
+				} else if strings.Contains(err.Error(), "ollama-mlx: no such file or directory") {
+					// most likely linux arm - not supported yet
+					t.Skip("unsupported architecture")
+				}
+				t.Fatalf("failed to generate image: %v", err)
+			}
+
+			imageData, err := base64.StdEncoding.DecodeString(imageBase64)
+			if err != nil {
+				t.Fatalf("failed to decode image: %v", err)
+			}
+			t.Logf("Generated image: %d bytes", len(imageData))
+
+			// Preload vision model and check GPU loading
+			err = client.Generate(ctx, &api.GenerateRequest{Model: tc.visionModel}, func(response api.GenerateResponse) error { return nil })
+			if err != nil {
+				t.Fatalf("failed to load vision model: %v", err)
+			}
+
+			// Use vision model to describe the image
+			chatReq := api.ChatRequest{
+				Model: tc.visionModel,
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "Describe this image in detail. What is shown? What style is it? What is the main subject doing?",
+						Images:  []api.ImageData{imageData},
+					},
+				},
+				Stream: &stream,
+				Options: map[string]any{
+					"seed":        42,
+					"temperature": 0.0,
+				},
+			}
+
+			// Verify the vision model's response contains expected keywords
+			response := DoChat(ctx, t, client, chatReq, tc.expectedWords, 240*time.Second, 30*time.Second)
+			if response != nil {
+				t.Logf("Vision model response: %s", response.Content)
+
+				// Additional detailed check for keywords
+				content := strings.ToLower(response.Content)
+				foundWords := []string{}
+				missingWords := []string{}
+				for _, word := range tc.expectedWords {
+					if strings.Contains(content, word) {
+						foundWords = append(foundWords, word)
+					} else {
+						missingWords = append(missingWords, word)
+					}
+				}
+				t.Logf("Found keywords: %v", foundWords)
+				if len(missingWords) > 0 {
+					t.Logf("Missing keywords (at least one was found so test passed): %v", missingWords)
+				}
+			}
+		})
+	}
+}
+
+// generateImage calls the Ollama API to generate an image and returns the base64 image data
+func generateImage(ctx context.Context, client *api.Client, model, prompt string) (string, error) {
+	var imageBase64 string
+
+	err := client.Generate(ctx, &api.GenerateRequest{
+		Model:  model,
+		Prompt: prompt,
+	}, func(resp api.GenerateResponse) error {
+		if resp.Image != "" {
+			imageBase64 = resp.Image
+		}
+		return nil
+	})
+	if err != nil {
+		return "", fmt.Errorf("failed to generate image: %w", err)
+	}
+
+	if imageBase64 == "" {
+		return "", fmt.Errorf("no image data in response")
+	}
+
+	return imageBase64, nil
+}

integration/utils_test.go

@@ -38,6 +38,7 @@ var (
 
 	// Note: add newer models at the top of the list to test them first
 	ollamaEngineChatModels = []string{
+		"lfm2.5-thinking",
 		"ministral-3",
 		"qwen3-coder:30b",
 		"gpt-oss:20b",
@@ -143,6 +144,7 @@ var (
 		"granite3.3",
 		"hermes3",
 		"internlm2",
+		"lfm2.5-thinking",
 		"llama-guard3",
 		"llama-pro",
 		"llama2-chinese",
@@ -263,6 +265,7 @@ var (
 		"snowflake-arctic-embed2",
 	}
 	libraryToolsModels = []string{
+		"lfm2.5-thinking",
 		"qwen3-vl",
 		"gpt-oss:20b",
 		"gpt-oss:120b",

ml/backend.go

@@ -162,6 +162,7 @@ type Tensor interface {
 	AvgPool2D(ctx Context, k, s int, p float32) Tensor
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 	Conv3D(ctx Context, weight Tensor, c, s0, s1, s2, p0, p1, p2, d0, d1, d2 int) Tensor
+	SSMConv(ctx Context, kernel Tensor) Tensor
 
 	IM2Col(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 

ml/backend/ggml/ggml.go

@@ -1641,6 +1641,13 @@ func (t *Tensor) Conv3D(ctx ml.Context, t2 ml.Tensor, c, s0, s1, s2, p0, p1, p2,
 	return tt
 }
 
+func (t *Tensor) SSMConv(ctx ml.Context, kernel ml.Tensor) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_ssm_conv(ctx.(*Context).ctx, t.t, kernel.(*Tensor).t),
+	}
+}
+
 func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,

model/models/lfm2/cache.go

@@ -0,0 +1,410 @@
+package lfm2
+
+import (
+	"slices"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
+)
+
+var _ kvcache.Cache = (*HybridCache)(nil)
+
+// HybridCache stores:
+// - a standard causal KV cache for attention layers
+// - a per-sequence recurrent conv state for shortconv layers
+//
+// Conv state shape (per layer, per sequence): [dConv, hiddenSize] where dConv = L_cache - 1.
+// Stored internally as a tensor of shape [dConv * hiddenSize, maxSlots].
+type HybridCache struct {
+	kv *kvcache.Causal
+
+	backend      ml.Backend
+	dtype        ml.DType
+	maxSequences int
+
+	hiddenSize int
+	dConv      int
+
+	// slot mapping for recurrent state
+	slotForSeq map[int]int
+	refCount   []int
+	freeSlots  []int
+
+	// per-layer conv state buffers (allocated lazily)
+	convCtxs   map[int]ml.Context
+	convStates map[int]ml.Tensor // [dConv*hiddenSize, maxSlots]
+
+	// current forward batch (derived in StartForward)
+	curSeqs       []int
+	curSlots      []int
+	curSlotsInput ml.Tensor
+	curSeqTokens  int
+
+	// track if EnsureWritable has been called for this forward pass
+	writableEnsured bool
+	// track any error from EnsureWritable to propagate later
+	writableError error
+}
+
+func NewHybridCache(shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error), hiddenSize, dConv int) *HybridCache {
+	return &HybridCache{
+		kv:         kvcache.NewCausalCache(shift),
+		hiddenSize: hiddenSize,
+		dConv:      dConv,
+		slotForSeq: make(map[int]int),
+		convCtxs:   make(map[int]ml.Context),
+		convStates: make(map[int]ml.Tensor),
+	}
+}
+
+func (c *HybridCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
+	c.backend = backend
+	c.dtype = dtype
+	c.maxSequences = maxSequences
+
+	// initialize slot allocator
+	c.refCount = make([]int, maxSequences)
+	c.freeSlots = c.freeSlots[:0]
+	for i := maxSequences - 1; i >= 0; i-- {
+		c.freeSlots = append(c.freeSlots, i)
+	}
+
+	c.kv.Init(backend, dtype, maxSequences, capacity, maxBatch)
+}
+
+func (c *HybridCache) Close() {
+	for _, ctx := range c.convCtxs {
+		ctx.Close()
+	}
+	c.kv.Close()
+}
+
+func (c *HybridCache) SetConfig(config ml.CacheConfig) {
+	c.kv.SetConfig(config)
+}
+
+func (c *HybridCache) SetLayer(layer int) {
+	c.kv.SetLayer(layer)
+}
+
+func (c *HybridCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
+	return c.kv.Get(ctx)
+}
+
+func (c *HybridCache) Put(ctx ml.Context, key, value ml.Tensor) {
+	c.kv.Put(ctx, key, value)
+}
+
+func (c *HybridCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
+	if err := c.kv.StartForward(ctx, batch, reserve); err != nil {
+		return err
+	}
+
+	// Derive equal-length sequence layout for shortconv.
+	// LFM2 shortconv assumes tokens form a [seq_tokens, seqs] grid.
+	seqCounts := make(map[int]int)
+	c.curSeqs = c.curSeqs[:0]
+	for _, s := range batch.Sequences {
+		if _, ok := seqCounts[s]; !ok {
+			c.curSeqs = append(c.curSeqs, s)
+		}
+		seqCounts[s]++
+	}
+
+	if len(c.curSeqs) == 0 {
+		return nil
+	}
+
+	nTokens := len(batch.Sequences)
+	nSeqs := len(c.curSeqs)
+	want := nTokens / nSeqs
+	for _, s := range c.curSeqs {
+		if seqCounts[s] != want {
+			return kvcache.ErrNotSupported
+		}
+	}
+
+	c.curSeqTokens = want
+
+	// When reserving memory for estimation, use fake slot assignments
+	// without modifying permanent state (slotForSeq, refCount)
+	if reserve {
+		c.curSlots = c.curSlots[:0]
+		slots := make([]int32, nSeqs)
+		for i := range nSeqs {
+			c.curSlots = append(c.curSlots, i)
+			slots[i] = int32(i)
+		}
+		c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+		return nil
+	}
+
+	// Ensure slots exist for sequences in this batch
+	c.curSlots = c.curSlots[:0]
+	var newSlots []int // track newly allocated slots that need zeroing
+	for _, s := range c.curSeqs {
+		slot, ok := c.slotForSeq[s]
+		if !ok {
+			var err error
+			slot, err = c.allocSlot()
+			if err != nil {
+				return err
+			}
+			c.slotForSeq[s] = slot
+			c.refCount[slot] = 1
+			newSlots = append(newSlots, slot)
+		}
+		c.curSlots = append(c.curSlots, slot)
+	}
+
+	// Zero conv state for newly allocated slots to clear stale data from previous sequences
+	if len(newSlots) > 0 {
+		c.zeroConvSlots(ctx, newSlots)
+	}
+
+	// Create a tensor for the current slots
+	slots := make([]int32, len(c.curSlots))
+	for i, v := range c.curSlots {
+		slots[i] = int32(v)
+	}
+	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+
+	// Reset writable state for new forward pass
+	c.writableEnsured = false
+	c.writableError = nil
+
+	return nil
+}
+
+func (c *HybridCache) allocSlot() (int, error) {
+	if len(c.freeSlots) == 0 {
+		return 0, kvcache.ErrKvCacheFull
+	}
+	slot := c.freeSlots[len(c.freeSlots)-1]
+	c.freeSlots = c.freeSlots[:len(c.freeSlots)-1]
+	return slot, nil
+}
+
+func (c *HybridCache) freeSlot(slot int) {
+	// Bounds check before freeing
+	if slot >= 0 && slot < c.maxSequences {
+		c.freeSlots = append(c.freeSlots, slot)
+	}
+}
+
+// zeroConvSlots zeros the conv state for the given slots across all layers.
+// This must be called when recycling slots to prevent stale state from affecting new sequences.
+func (c *HybridCache) zeroConvSlots(ctx ml.Context, slots []int) {
+	if len(slots) == 0 || len(c.convStates) == 0 {
+		return
+	}
+
+	// Use input context for creating tensors
+	inputCtx := ctx.Input()
+
+	// Create slot indices tensor
+	slotIndices := make([]int32, len(slots))
+	for i, s := range slots {
+		slotIndices[i] = int32(s)
+	}
+	slotsTensor := inputCtx.FromInts(slotIndices, len(slotIndices))
+
+	// Create zero tensor for the slots (SetRows requires F32 source)
+	zeros := inputCtx.Zeros(ml.DTypeF32, c.dConv*c.hiddenSize, len(slots))
+
+	// Zero each layer's conv state for these slots
+	for _, buf := range c.convStates {
+		ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
+	}
+}
+
+// EnsureWritable ensures that sequences in the current batch have private (non-shared) conv slots.
+// Returns an error if slot allocation fails.
+func (c *HybridCache) EnsureWritable(ctx ml.Context) error {
+	for i, seq := range c.curSeqs {
+		slot, ok := c.slotForSeq[seq]
+		if !ok {
+			continue
+		}
+
+		// Bounds check
+		if slot < 0 || slot >= len(c.refCount) {
+			continue
+		}
+
+		if c.refCount[slot] <= 1 {
+			continue
+		}
+
+		newSlot, err := c.allocSlot()
+		if err != nil {
+			return err
+		}
+		c.refCount[slot]--
+		c.refCount[newSlot] = 1
+		c.slotForSeq[seq] = newSlot
+		c.curSlots[i] = newSlot
+
+		// Copy existing conv state for all initialized layers
+		for _, buf := range c.convStates {
+			// buf: [dConv*hiddenSize, maxSlots]
+			src := buf.Rows(ctx, ctx.Input().FromInts([]int32{int32(slot)}, 1))
+			// SetRows requires F32 source
+			srcF32 := src.Cast(ctx, ml.DTypeF32)
+			ctx.Forward(buf.SetRows(ctx, srcF32, ctx.Input().FromInts([]int32{int32(newSlot)}, 1)))
+		}
+	}
+
+	// Rebuild current slots tensor
+	slots := make([]int32, len(c.curSlots))
+	for i, v := range c.curSlots {
+		slots[i] = int32(v)
+	}
+	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+
+	return nil
+}
+
+func (c *HybridCache) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
+	// KV cache shares prefix metadata (no copy) which is correct for prefix reuse.
+	c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
+
+	// For shortconv state we implement copy-on-write: dst shares the same slot as src.
+	// On the first write to dst, EnsureWritable will create a private slot.
+	if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
+		// Bounds check before decrementing
+		if dstSlot >= 0 && dstSlot < len(c.refCount) {
+			c.refCount[dstSlot]--
+			if c.refCount[dstSlot] <= 0 {
+				c.refCount[dstSlot] = 0
+				c.freeSlot(dstSlot)
+			}
+		}
+		delete(c.slotForSeq, dstSeq)
+	}
+
+	srcSlot, ok := c.slotForSeq[srcSeq]
+	if !ok {
+		// src may not have a slot yet; dst will allocate on demand
+		return
+	}
+
+	// Bounds check before incrementing
+	if srcSlot >= 0 && srcSlot < len(c.refCount) {
+		c.slotForSeq[dstSeq] = srcSlot
+		c.refCount[srcSlot]++
+	}
+}
+
+func (c *HybridCache) CanResume(seq int, pos int32) bool {
+	return c.kv.CanResume(seq, pos)
+}
+
+func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
+		return err
+	}
+
+	// For recurrent state, any removal invalidates the state because
+	// the state at position N depends on all previous positions.
+	// Drop the slot mapping so it resets on next use.
+	slot, ok := c.slotForSeq[seq]
+	if !ok {
+		return nil
+	}
+
+	// Bounds check
+	if slot < 0 || slot >= len(c.refCount) {
+		delete(c.slotForSeq, seq)
+		return nil
+	}
+
+	c.refCount[slot]--
+	if c.refCount[slot] <= 0 {
+		c.refCount[slot] = 0
+		c.freeSlot(slot)
+	}
+	delete(c.slotForSeq, seq)
+
+	return nil
+}
+
+func (c *HybridCache) slotsTensor() ml.Tensor {
+	return c.curSlotsInput
+}
+
+func (c *HybridCache) seqTokens() int {
+	return c.curSeqTokens
+}
+
+func (c *HybridCache) numSeqs() int {
+	return len(c.curSeqs)
+}
+
+func (c *HybridCache) convBuffer(ctx ml.Context, layer int) ml.Tensor {
+	if buf, ok := c.convStates[layer]; ok {
+		return buf
+	}
+
+	if _, ok := c.convCtxs[layer]; !ok {
+		c.convCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
+	}
+
+	buf := c.convCtxs[layer].Zeros(c.dtype, c.dConv*c.hiddenSize, c.maxSequences)
+	c.convStates[layer] = buf
+	return buf
+}
+
+// ConvState returns the conv state for current batch sequences as shape [dConv, hiddenSize, nSeqs].
+// Returns an error if copy-on-write allocation fails.
+func (c *HybridCache) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
+	if !c.writableEnsured {
+		needsWritable := false
+		for _, seq := range c.curSeqs {
+			slot, ok := c.slotForSeq[seq]
+			if !ok {
+				continue
+			}
+			if slot >= 0 && slot < len(c.refCount) && c.refCount[slot] > 1 {
+				needsWritable = true
+				break
+			}
+		}
+
+		if needsWritable {
+			if err := c.EnsureWritable(ctx); err != nil {
+				c.writableError = err
+			}
+		}
+		c.writableEnsured = true
+	}
+
+	if c.writableError != nil {
+		return nil, c.writableError
+	}
+
+	buf := c.convBuffer(ctx, layer)
+	cur := buf.Rows(ctx, c.slotsTensor())
+	return cur.Reshape(ctx, c.dConv, c.hiddenSize, c.numSeqs()), nil
+}
+
+// UpdateConvState writes a new conv state for current batch sequences.
+// newState must have shape [dConv, hiddenSize, nSeqs].
+func (c *HybridCache) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
+	buf := c.convBuffer(ctx, layer)
+	src := newState.Reshape(ctx, c.dConv*c.hiddenSize, c.numSeqs())
+	// SetRows requires F32 source
+	srcF32 := src.Cast(ctx, ml.DTypeF32)
+	ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
+}
+
+// IsSupportedForBatch returns true if the current batch layout supports shortconv.
+func (c *HybridCache) IsSupportedForBatch() bool {
+	return c.curSeqTokens > 0 && len(c.curSeqs) > 0
+}
+
+// Seqs returns the ordered unique sequences for the current forward pass.
+func (c *HybridCache) Seqs() []int {
+	return slices.Clone(c.curSeqs)
+}

model/models/lfm2/cache_test.go

@@ -0,0 +1,444 @@
+package lfm2
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+)
+
+// TestHybridCache tests verify the slot management logic of HybridCache.
+// These tests focus on the recurrent state slot allocation, reference counting,
+// and copy-on-write semantics without requiring a full ML backend.
+
+// createSlotOnlyCache creates a HybridCache with only the slot management
+// fields initialized. Used to test slot logic in isolation.
+func createSlotOnlyCache(maxSequences int) *HybridCache {
+	return &HybridCache{
+		hiddenSize:   256,
+		dConv:        3,
+		maxSequences: maxSequences,
+		refCount:     make([]int, maxSequences),
+		freeSlots:    initFreeSlots(maxSequences),
+		slotForSeq:   make(map[int]int),
+		convCtxs:     make(map[int]ml.Context),
+		convStates:   make(map[int]ml.Tensor),
+	}
+}
+
+func initFreeSlots(n int) []int {
+	slots := make([]int, 0, n)
+	for i := n - 1; i >= 0; i-- {
+		slots = append(slots, i)
+	}
+	return slots
+}
+
+func TestHybridCache_SlotAllocation(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Verify initial state
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate all slots
+	for range 4 {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.refCount[slot] = 1
+	}
+
+	// Should be full now
+	if len(cache.freeSlots) != 0 {
+		t.Errorf("expected 0 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Trying to allocate another should fail
+	_, err := cache.allocSlot()
+	if err != kvcache.ErrKvCacheFull {
+		t.Errorf("expected ErrKvCacheFull, got %v", err)
+	}
+}
+
+func TestHybridCache_SlotReuse(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate a slot
+	slot1, _ := cache.allocSlot()
+	cache.refCount[slot1] = 1
+
+	// Free it
+	cache.refCount[slot1] = 0
+	cache.freeSlot(slot1)
+
+	// Allocate again - should get the same slot back (LIFO)
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected slot %d to be reused, got %d", slot1, slot2)
+	}
+}
+
+func TestHybridCache_SlotRefCounting_ShareSlot(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Simulate sharing slot with seq 2 (copy-on-write style)
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Should share the same slot
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Ref count should be 2
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotRefCounting_DecRef(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share with seq 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Unshare seq 2
+	cache.refCount[slot1]--
+	delete(cache.slotForSeq, 2)
+
+	// Ref count should be back to 1
+	if cache.refCount[slot1] != 1 {
+		t.Errorf("expected refCount 1 after unshare, got %d", cache.refCount[slot1])
+	}
+
+	// Seq 2 should no longer have a slot
+	if _, ok := cache.slotForSeq[2]; ok {
+		t.Error("seq 2 should not have a slot after unshare")
+	}
+}
+
+func TestHybridCache_SlotFreeWhenUnused(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot when refCount drops to 0
+	cache.refCount[slot1]--
+	if cache.refCount[slot1] <= 0 {
+		cache.refCount[slot1] = 0
+		cache.freeSlot(slot1)
+	}
+	delete(cache.slotForSeq, 1)
+
+	// Slot should be freed
+	if len(cache.freeSlots) != initialFreeSlots {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots, len(cache.freeSlots))
+	}
+
+	// Ref count should be 0
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotOverwrite(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slots for seq 1 and seq 2
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	slot2, _ := cache.allocSlot()
+	cache.slotForSeq[2] = slot2
+	cache.refCount[slot2] = 1
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Simulate overwriting seq 2's slot with slot1 (sharing)
+	// First free the old slot
+	cache.refCount[slot2]--
+	if cache.refCount[slot2] <= 0 {
+		cache.refCount[slot2] = 0
+		cache.freeSlot(slot2)
+	}
+	// Then share slot1
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Seq 2 should now share slot1
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Old slot2 should be freed
+	if len(cache.freeSlots) != initialFreeSlots+1 {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots+1, len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_BoundsChecking(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Test freeing invalid slot (should not panic)
+	cache.freeSlot(-1)
+	cache.freeSlot(100) // out of bounds
+
+	// freeSlot does bounds checking, so invalid slots should be ignored
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("invalid slots should not affect free list, got %d slots", len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_MultipleSequences_RefCounting(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Fork to seq 2, 3, 4 (all share slot1)
+	for _, seq := range []int{2, 3, 4} {
+		cache.slotForSeq[seq] = slot1
+		cache.refCount[slot1]++
+	}
+
+	// Ref count should be 4
+	if cache.refCount[slot1] != 4 {
+		t.Errorf("expected refCount 4, got %d", cache.refCount[slot1])
+	}
+
+	// Remove seq 2, 3
+	for _, seq := range []int{2, 3} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+
+	// Slot should still be allocated (not in free list)
+	found := false
+	for _, s := range cache.freeSlots {
+		if s == slot1 {
+			found = true
+			break
+		}
+	}
+	if found {
+		t.Error("slot1 should not be in free list yet")
+	}
+
+	// Remove remaining sequences
+	for _, seq := range []int{1, 4} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_ChainedSharing(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Create seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share 1 -> 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Share 2 -> 3 (should still share slot1)
+	cache.slotForSeq[3] = cache.slotForSeq[2] // which is slot1
+	cache.refCount[slot1]++
+
+	// All should share slot1
+	if cache.slotForSeq[1] != slot1 || cache.slotForSeq[2] != slot1 || cache.slotForSeq[3] != slot1 {
+		t.Error("all sequences should share slot1")
+	}
+
+	if cache.refCount[slot1] != 3 {
+		t.Errorf("expected refCount 3, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_CacheParameters(t *testing.T) {
+	cache := NewHybridCache(nil, 512, 5) // hiddenSize=512, dConv=5
+
+	if cache.hiddenSize != 512 {
+		t.Errorf("expected hiddenSize 512, got %d", cache.hiddenSize)
+	}
+	if cache.dConv != 5 {
+		t.Errorf("expected dConv 5, got %d", cache.dConv)
+	}
+}
+
+func TestHybridCache_NumSeqs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially no sequences
+	if cache.numSeqs() != 0 {
+		t.Errorf("expected 0 seqs, got %d", cache.numSeqs())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqs = []int{1, 2, 3}
+
+	if cache.numSeqs() != 3 {
+		t.Errorf("expected 3 seqs, got %d", cache.numSeqs())
+	}
+}
+
+func TestHybridCache_SeqTokens(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially 0
+	if cache.seqTokens() != 0 {
+		t.Errorf("expected 0 seqTokens, got %d", cache.seqTokens())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqTokens = 16
+
+	if cache.seqTokens() != 16 {
+		t.Errorf("expected 16 seqTokens, got %d", cache.seqTokens())
+	}
+}
+
+// Test that Seqs returns a clone of curSeqs
+func TestHybridCache_Seqs_ReturnsClone(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	cache.curSeqs = []int{1, 2, 3}
+
+	seqs := cache.Seqs()
+
+	// Modify returned slice
+	seqs[0] = 999
+
+	// Original should be unchanged
+	if cache.curSeqs[0] != 1 {
+		t.Error("Seqs should return a clone, not the original slice")
+	}
+}
+
+func TestHybridCache_IsSupportedForBatch(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially not supported (no batch set up)
+	if cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be false initially")
+	}
+
+	// Set up a valid batch
+	cache.curSeqTokens = 1
+	cache.curSeqs = []int{1}
+
+	if !cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be true with valid batch")
+	}
+}
+
+func TestHybridCache_ZeroConvSlots_EmptyInputs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// zeroConvSlots should handle empty slots without panicking
+	cache.zeroConvSlots(nil, nil)
+	cache.zeroConvSlots(nil, []int{})
+
+	// zeroConvSlots should handle empty convStates without panicking
+	cache.zeroConvSlots(nil, []int{0, 1, 2})
+}
+
+func TestHybridCache_SlotRecycling_TracksNewSlots(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot (simulating sequence removal)
+	cache.refCount[slot1]--
+	cache.freeSlot(slot1)
+	delete(cache.slotForSeq, 1)
+
+	// Verify slot is in free list
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots after freeing, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate for new seq 2 - should get recycled slot
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected recycled slot %d, got %d", slot1, slot2)
+	}
+
+	// This recycled slot would need zeroing in the real implementation
+	// The actual zeroing is tested via integration tests since it requires ML context
+}
+
+func TestHybridCache_NewSequence_GetsTrackedForZeroing(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Simulate the slot allocation flow from StartForward
+	// When a sequence doesn't have a slot, it gets allocated and tracked as "new"
+
+	newSlots := []int{}
+
+	// Seq 1 doesn't have a slot - allocate and track
+	seq := 1
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots = append(newSlots, slot)
+	}
+
+	// Verify newSlots contains the allocated slot
+	if len(newSlots) != 1 {
+		t.Errorf("expected 1 new slot, got %d", len(newSlots))
+	}
+
+	// Seq 1 already has a slot - should NOT be tracked as new
+	newSlots2 := []int{}
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, _ := cache.allocSlot()
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots2 = append(newSlots2, slot)
+	}
+
+	// Verify no new slots for existing sequence
+	if len(newSlots2) != 0 {
+		t.Errorf("expected 0 new slots for existing sequence, got %d", len(newSlots2))
+	}
+}

model/models/lfm2/model.go

@@ -0,0 +1,253 @@
+package lfm2
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	hiddenSize       int
+	headDim, ropeDim int
+
+	eps, ropeBase, ropeScale float32
+
+	ropeType              string
+	originalContextLength int
+
+	// per-layer head counts (LFM2 alternates attention and recurrent layers)
+	numHeadsByLayer   []int
+	numKVHeadsByLayer []int
+}
+
+func (o Options) headDimValue() int {
+	// Head dim is shared across layers; fall back to first attention layer head count.
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			return cmp.Or(o.headDim, o.hiddenSize/h)
+		}
+	}
+	return cmp.Or(o.headDim, o.hiddenSize)
+}
+
+func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
+	opts := []func(*rope.Options){rope.WithTypeNeoX()}
+	if o.ropeType == "yarn" {
+		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
+		opts = append(opts,
+			rope.WithOriginalContextLength(o.originalContextLength),
+			rope.WithExtrapolationFactor(1.),
+			rope.WithAttentionFactor(attnFactor),
+		)
+	}
+
+	headCount := 1
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			headCount = h
+			break
+		}
+	}
+	return nn.RoPE(ctx, states, positions, cmp.Or(o.ropeDim, o.headDim, o.hiddenSize/headCount), o.ropeBase, 1./o.ropeScale, opts...)
+}
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm,alt:token_embd_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	if c.Uint("expert_count") > 0 {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	if c.String("tokenizer.ggml.model") != "gpt2" {
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	vocabulary := model.Vocabulary{
+		Values: c.Strings("tokenizer.ggml.tokens"),
+		Scores: c.Floats("tokenizer.ggml.scores"),
+		Types:  c.Ints("tokenizer.ggml.token_type"),
+		Merges: c.Strings("tokenizer.ggml.merges"),
+		AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+		BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+		AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+		EOS: append(
+			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+			c.Ints("tokenizer.ggml.eos_token_ids")...,
+		),
+	}
+
+	var pretokenizers []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "default":
+		// use default BPE pretokenizer
+	default:
+		// llama-bpe style (default for LFM2)
+		pretokenizers = []string{
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		}
+	}
+
+	m := Model{
+		TextProcessor: model.NewBytePairEncoding(&vocabulary, pretokenizers...),
+		Layers:        make([]Layer, c.Uint("block_count")),
+		Options: Options{
+			hiddenSize:            int(c.Uint("embedding_length")),
+			headDim:               int(c.Uint("attention.key_length")),
+			ropeDim:               int(c.Uint("rope.dimension_count")),
+			eps:                   c.Float("attention.layer_norm_rms_epsilon"),
+			ropeType:              c.String("rope.scaling.type"),
+			ropeBase:              c.Float("rope.freq_base"),
+			ropeScale:             c.Float("rope.scaling.factor", 1),
+			originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
+		},
+	}
+
+	type headCounts interface {
+		HeadCount() []uint64
+		HeadCountKV() []uint64
+	}
+	hc, ok := c.(headCounts)
+	if !ok {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	headCount := hc.HeadCount()
+	headCountKV := hc.HeadCountKV()
+
+	m.numHeadsByLayer = make([]int, len(m.Layers))
+	m.numKVHeadsByLayer = make([]int, len(m.Layers))
+	for i := range m.Layers {
+		m.numHeadsByLayer[i] = int(headCount[i])
+		m.numKVHeadsByLayer[i] = int(headCountKV[i])
+
+		if m.numKVHeadsByLayer[i] == 0 {
+			m.Layers[i].Operator = &ShortConv{}
+		} else {
+			m.Layers[i].Operator = &Attention{}
+		}
+	}
+
+	lCache := int(c.Uint("shortconv.l_cache"))
+	dConv := max(0, lCache-1)
+	m.Cache = NewHybridCache(m.Shift, m.hiddenSize, dConv)
+	return &m, nil
+}
+
+type Operator interface {
+	Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor
+}
+
+type Attention struct {
+	Query     *nn.Linear  `gguf:"attn_q"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
+	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	Value     *nn.Linear  `gguf:"attn_v"`
+	Output    *nn.Linear  `gguf:"attn_output,alt:attn_out"`
+}
+
+func (sa *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+	headDim := opts.headDimValue()
+	numHeads := opts.numHeadsByLayer[layer]
+	numKVHeads := opts.numKVHeadsByLayer[layer]
+
+	query := sa.Query.Forward(ctx, hiddenStates)
+	key := sa.Key.Forward(ctx, hiddenStates)
+	value := sa.Value.Forward(ctx, hiddenStates)
+
+	query = query.Reshape(ctx, headDim, numHeads, batchSize)
+	key = key.Reshape(ctx, headDim, numKVHeads, batchSize)
+	value = value.Reshape(ctx, headDim, numKVHeads, batchSize)
+
+	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
+	key = sa.KeyNorm.Forward(ctx, key, opts.eps)
+
+	query = opts.applyRotaryPositionEmbeddings(ctx, query, positions)
+	key = opts.applyRotaryPositionEmbeddings(ctx, key, positions)
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), cache)
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
+	return sa.Output.Forward(ctx, attention)
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Operator      Operator
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, layer int, hiddenState, positions, outputs ml.Tensor, cache *HybridCache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.Operator.Forward(ctx, hiddenState, positions, cache, layer, opts)
+
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, outputs, m.Cache.(*HybridCache), &m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState), nil
+}
+
+func init() {
+	model.Register("lfm2", New)
+}

model/models/lfm2/shortconv.go

@@ -0,0 +1,50 @@
+package lfm2
+
+import (
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type shortConvKernel struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+// ShortConv implements the LFM2 short-convolution block (GGML_OP_SSM_CONV) with a recurrent
+// state stored in the HybridCache.
+type ShortConv struct {
+	Conv    *shortConvKernel `gguf:"shortconv.conv"`
+	InProj  *nn.Linear       `gguf:"shortconv.in_proj"`
+	OutProj *nn.Linear       `gguf:"shortconv.out_proj"`
+}
+
+func (sc *ShortConv) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	nSeqs := cache.numSeqs()
+	seqTokens := cache.seqTokens()
+	hiddenSize := hiddenStates.Dim(0)
+	if nSeqs <= 0 || seqTokens <= 0 || hiddenStates.Dim(1) != nSeqs*seqTokens {
+		panic("lfm2: unsupported batch layout for shortconv")
+	}
+
+	bcx := sc.InProj.Forward(ctx, hiddenStates).Reshape(ctx, 3*hiddenSize, seqTokens, nSeqs)
+
+	elementSize := bcx.Stride(0)
+	b := bcx.View(ctx, 0*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	c := bcx.View(ctx, 1*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	x := bcx.View(ctx, 2*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+
+	bx := b.Mul(ctx, x).Permute(ctx, 1, 0, 2, 3)
+
+	state, err := cache.ConvState(ctx, layer)
+	if err != nil {
+		panic("lfm2: failed to get conv state: " + err.Error())
+	}
+	sx := state.Concat(ctx, bx, 0)
+
+	convOut := sx.SSMConv(ctx, sc.Conv.Weight)
+	y := c.Mul(ctx, convOut)
+
+	dConv := sx.Dim(0) - seqTokens
+	cache.UpdateConvState(ctx, layer, sx.Slice(ctx, 0, sx.Dim(0)-dConv, sx.Dim(0), 1))
+
+	return sc.OutProj.Forward(ctx, y.Reshape(ctx, hiddenSize, seqTokens*nSeqs))
+}

model/models/models.go

@@ -9,6 +9,7 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma3n"
 	_ "github.com/ollama/ollama/model/models/glm4moelite"
 	_ "github.com/ollama/ollama/model/models/gptoss"
+	_ "github.com/ollama/ollama/model/models/lfm2"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"

model/parsers/lfm2.go

@@ -0,0 +1,498 @@
+package parsers
+
+import (
+	"encoding/json"
+	"errors"
+	"log/slog"
+	"strconv"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2ParserState int
+
+const (
+	LFM2CollectingThinking LFM2ParserState = iota
+	LFM2CollectingContent
+	LFM2CollectingToolCalls
+)
+
+const (
+	lfm2ThinkingOpenTag  = "<think>"
+	lfm2ThinkingCloseTag = "</think>"
+	lfm2ToolCallStartTag = "<|tool_call_start|>"
+	lfm2ToolCallEndTag   = "<|tool_call_end|>"
+)
+
+type LFM2Parser struct {
+	state                    LFM2ParserState
+	buffer                   strings.Builder
+	hasThinkingSupport       bool
+	needsThinkingLeadingTrim bool // trim leading whitespace after <think> tag
+	needsContentLeadingTrim  bool // trim leading whitespace after </think> tag
+}
+
+func (p *LFM2Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *LFM2Parser) HasThinkingSupport() bool {
+	return p.hasThinkingSupport
+}
+
+func (p *LFM2Parser) setInitialState(lastMessage *api.Message, thinkValue *api.ThinkValue) {
+	prefill := lastMessage != nil && lastMessage.Role == "assistant"
+
+	// Check both model capability AND request preference
+	thinkingEnabled := p.HasThinkingSupport() && (thinkValue != nil && thinkValue.Bool())
+
+	if !thinkingEnabled {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	if prefill && lastMessage.Content != "" {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	p.state = LFM2CollectingThinking
+	p.needsThinkingLeadingTrim = true
+}
+
+func (p *LFM2Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.setInitialState(lastMessage, thinkValue)
+	return tools
+}
+
+type lfm2Event interface {
+	isLFM2Event()
+}
+
+type lfm2EventThinkingContent struct {
+	content string
+}
+
+type lfm2EventContent struct {
+	content string
+}
+
+type lfm2EventToolCall struct {
+	toolCall api.ToolCall
+}
+
+func (lfm2EventThinkingContent) isLFM2Event() {}
+func (lfm2EventContent) isLFM2Event()         {}
+func (lfm2EventToolCall) isLFM2Event()        {}
+
+func (p *LFM2Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case lfm2EventToolCall:
+			toolCalls = append(toolCalls, event.toolCall)
+		case lfm2EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case lfm2EventContent:
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *LFM2Parser) parseEvents() []lfm2Event {
+	var all []lfm2Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []lfm2Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	return all
+}
+
+func (p *LFM2Parser) eat() ([]lfm2Event, bool) {
+	var events []lfm2Event
+	bufStr := p.buffer.String()
+	if bufStr == "" {
+		return events, false
+	}
+
+	switch p.state {
+	case LFM2CollectingThinking:
+		// Strip opening <think> tag if present
+		if strings.HasPrefix(bufStr, lfm2ThinkingOpenTag) {
+			bufStr = bufStr[len(lfm2ThinkingOpenTag):]
+			p.needsThinkingLeadingTrim = true
+			p.buffer.Reset()
+			p.buffer.WriteString(bufStr)
+		}
+
+		// Trim leading whitespace after <think> tag (may span multiple chunks)
+		if p.needsThinkingLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content or buffer is empty
+			if len(bufStr) > 0 {
+				p.needsThinkingLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ThinkingCloseTag) { // thinking[</think>] -> content
+			split := strings.SplitN(bufStr, lfm2ThinkingCloseTag, 2)
+			thinking := split[0]
+			thinking = strings.TrimRightFunc(thinking, unicode.IsSpace)
+
+			remaining := split[1]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingContent
+			p.needsThinkingLeadingTrim = false
+			// Set flag to trim any additional whitespace that may arrive in later chunks
+			p.needsContentLeadingTrim = len(remaining) == 0
+
+			if len(thinking) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: thinking})
+			}
+			return events, true
+		} else if overlapLen := overlap(bufStr, lfm2ThinkingCloseTag); overlapLen > 0 { // partial </think>
+			beforePartialTag := bufStr[:len(bufStr)-overlapLen]
+			trailingLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else { // otherwise its thinking content
+			whitespaceLen := trailingWhitespaceLen(bufStr)
+			ambiguousStart := len(bufStr) - whitespaceLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingContent:
+		// Trim leading whitespace after </think> tag (may span multiple chunks)
+		if p.needsContentLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content
+			if len(bufStr) > 0 {
+				p.needsContentLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ToolCallStartTag) { // content[<|tool_call_start|>] -> tool calls
+			split := strings.SplitN(bufStr, lfm2ToolCallStartTag, 2)
+			contentBefore := strings.TrimRightFunc(split[0], unicode.IsSpace)
+			remaining := split[1]
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingToolCalls
+
+			if len(contentBefore) > 0 {
+				events = append(events, lfm2EventContent{content: contentBefore})
+			}
+			return events, true
+		} else { // otherwise its content
+			p.buffer.Reset()
+			if len(bufStr) > 0 {
+				events = append(events, lfm2EventContent{content: bufStr})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingToolCalls:
+		// Look for complete tool call JSON between tags
+		if idx := strings.Index(bufStr, lfm2ToolCallEndTag); idx != -1 {
+			toolCallContent := bufStr[:idx]
+
+			if toolCalls, err := p.parseToolCallsContent(toolCallContent); err == nil && len(toolCalls) > 0 {
+				remaining := bufStr[idx+len(lfm2ToolCallEndTag):]
+
+				// Check if there's another tool call
+				if strings.HasPrefix(remaining, lfm2ToolCallStartTag) {
+					remaining = remaining[len(lfm2ToolCallStartTag):]
+				} else {
+					// No more tool calls, go back to content
+					remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+					p.state = LFM2CollectingContent
+				}
+
+				p.buffer.Reset()
+				p.buffer.WriteString(remaining)
+
+				for _, tc := range toolCalls {
+					events = append(events, lfm2EventToolCall{toolCall: tc})
+				}
+				return events, true
+			} else if err != nil {
+				slog.Warn("lfm2 tool call parsing failed", "error", err, "content", toolCallContent)
+			}
+		}
+
+		return events, false
+	}
+
+	return events, false
+}
+
+// parseToolCallsContent parses one or more tool calls from content
+// Supports JSON format and Python-style format including multiple calls: [func1(...),func2(...)]
+func (p *LFM2Parser) parseToolCallsContent(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Try JSON format first: {"name": "func", "arguments": {...}}
+	var parsed struct {
+		Name      string          `json:"name"`
+		Arguments json.RawMessage `json:"arguments"`
+	}
+
+	if err := json.Unmarshal([]byte(content), &parsed); err == nil && parsed.Name != "" {
+		var args api.ToolCallFunctionArguments
+		if len(parsed.Arguments) > 0 {
+			if err := json.Unmarshal(parsed.Arguments, &args); err != nil {
+				return nil, err
+			}
+		} else {
+			args = api.NewToolCallFunctionArguments()
+		}
+
+		return []api.ToolCall{{
+			Function: api.ToolCallFunction{
+				Name:      parsed.Name,
+				Arguments: args,
+			},
+		}}, nil
+	}
+
+	// Try Python-style format: [func(arg1='val1'),func2(arg2='val2')] or func(arg1='val1')
+	return p.parsePythonStyleToolCalls(content)
+}
+
+// parsePythonStyleToolCalls parses one or more Python-style tool calls
+// Examples: [bash(command='ls'),bash(command='pwd')] or bash(command='ls')
+func (p *LFM2Parser) parsePythonStyleToolCalls(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Strip outer brackets if present: [func(...)] -> func(...)
+	if strings.HasPrefix(content, "[") && strings.HasSuffix(content, "]") {
+		content = content[1 : len(content)-1]
+	}
+
+	var toolCalls []api.ToolCall
+
+	// Parse multiple function calls separated by commas at the top level
+	for len(content) > 0 {
+		content = strings.TrimSpace(content)
+		if content == "" {
+			break
+		}
+
+		// Skip leading comma from previous iteration
+		if strings.HasPrefix(content, ",") {
+			content = strings.TrimSpace(content[1:])
+			if content == "" {
+				break
+			}
+		}
+
+		// Find function name
+		parenIdx := strings.Index(content, "(")
+		if parenIdx == -1 {
+			return nil, errors.New("invalid tool call: no opening parenthesis")
+		}
+
+		funcName := strings.TrimSpace(content[:parenIdx])
+		if funcName == "" {
+			return nil, errors.New("invalid tool call: empty function name")
+		}
+
+		// Find matching closing parenthesis
+		closeIdx := findMatchingParen(content, parenIdx)
+		if closeIdx == -1 {
+			return nil, errors.New("invalid tool call: no matching closing parenthesis")
+		}
+
+		argsStr := content[parenIdx+1 : closeIdx]
+		args := api.NewToolCallFunctionArguments()
+
+		if argsStr != "" {
+			if err := parsePythonArgs(argsStr, &args); err != nil {
+				return nil, err
+			}
+		}
+
+		toolCalls = append(toolCalls, api.ToolCall{
+			Function: api.ToolCallFunction{
+				Name:      funcName,
+				Arguments: args,
+			},
+		})
+
+		// Move past this function call
+		content = content[closeIdx+1:]
+	}
+
+	if len(toolCalls) == 0 {
+		return nil, errors.New("no tool calls found")
+	}
+
+	return toolCalls, nil
+}
+
+// findMatchingParen finds the index of the closing parenthesis matching the one at openIdx
+// Returns -1 if not found. Handles nested parentheses and quoted strings.
+func findMatchingParen(s string, openIdx int) int {
+	depth := 1
+	i := openIdx + 1
+	for i < len(s) && depth > 0 {
+		switch s[i] {
+		case '(':
+			depth++
+		case ')':
+			depth--
+			if depth == 0 {
+				return i
+			}
+		case '\'', '"':
+			// Skip quoted string
+			quote := s[i]
+			i++
+			for i < len(s) && s[i] != quote {
+				if s[i] == '\\' && i+1 < len(s) {
+					i++ // skip escaped char
+				}
+				i++
+			}
+		}
+		i++
+	}
+	return -1
+}
+
+// parseToolCallContent parses a single tool call (for backward compatibility with tests)
+func (p *LFM2Parser) parseToolCallContent(content string) (api.ToolCall, error) {
+	calls, err := p.parseToolCallsContent(content)
+	if err != nil {
+		return api.ToolCall{}, err
+	}
+	if len(calls) == 0 {
+		return api.ToolCall{}, errors.New("no tool call found")
+	}
+	return calls[0], nil
+}
+
+// parsePythonArgs parses Python-style keyword arguments: key='value', key2="value2"
+func parsePythonArgs(argsStr string, args *api.ToolCallFunctionArguments) error {
+	// Simple state machine to parse key='value' pairs
+	// Handles: command='ls', flag="-la", count=42, enabled=true
+	var key string
+	i := 0
+
+	for i < len(argsStr) {
+		// Skip whitespace
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+		if i >= len(argsStr) {
+			break
+		}
+
+		// Parse key
+		keyStart := i
+		for i < len(argsStr) && argsStr[i] != '=' && argsStr[i] != ',' {
+			i++
+		}
+		if i >= len(argsStr) || argsStr[i] != '=' {
+			return errors.New("invalid argument: expected '='")
+		}
+		key = strings.TrimSpace(argsStr[keyStart:i])
+		i++ // skip '='
+
+		// Skip whitespace after =
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t') {
+			i++
+		}
+
+		// Parse value
+		var value string
+		if i < len(argsStr) && (argsStr[i] == '\'' || argsStr[i] == '"') {
+			// Quoted string
+			quote := argsStr[i]
+			i++
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != quote {
+				if argsStr[i] == '\\' && i+1 < len(argsStr) {
+					i += 2 // skip escaped char
+				} else {
+					i++
+				}
+			}
+			value = argsStr[valueStart:i]
+			if i < len(argsStr) {
+				i++ // skip closing quote
+			}
+			args.Set(key, value)
+		} else {
+			// Unquoted value (number, bool, etc)
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != ',' {
+				i++
+			}
+			value = strings.TrimSpace(argsStr[valueStart:i])
+
+			// Try to parse as number or bool
+			if v, err := strconv.ParseInt(value, 10, 64); err == nil {
+				args.Set(key, v)
+			} else if v, err := strconv.ParseFloat(value, 64); err == nil {
+				args.Set(key, v)
+			} else if value == "true" {
+				args.Set(key, true)
+			} else if value == "false" {
+				args.Set(key, false)
+			} else {
+				args.Set(key, value)
+			}
+		}
+
+		// Skip comma and whitespace
+		for i < len(argsStr) && (argsStr[i] == ',' || argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+	}
+
+	return nil
+}

model/parsers/parsers.go

@@ -70,6 +70,10 @@ func ParserForName(name string) Parser {
 		return &FunctionGemmaParser{}
 	case "glm-4.7":
 		return &GLM47Parser{}
+	case "lfm2":
+		return &LFM2Parser{hasThinkingSupport: false}
+	case "lfm2-thinking":
+		return &LFM2Parser{hasThinkingSupport: true}
 	default:
 		return nil
 	}

model/renderers/lfm2.go

@@ -0,0 +1,144 @@
+package renderers
+
+import (
+	"encoding/json"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2Renderer struct {
+	IsThinking bool
+}
+
+func (r *LFM2Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	// Note: BOS token is added by the tokenizer (add_bos_token: true), not the renderer
+
+	// Extract first system message if present (to combine with tools)
+	var firstSystemContent string
+	startIdx := 0
+	if len(messages) > 0 && messages[0].Role == "system" {
+		firstSystemContent = messages[0].Content
+		startIdx = 1
+	}
+
+	// Append tools to first system content
+	if len(tools) > 0 {
+		if firstSystemContent != "" {
+			firstSystemContent += "\n"
+		}
+		firstSystemContent += "List of tools: ["
+		for i, tool := range tools {
+			toolJSON, err := json.Marshal(tool)
+			if err != nil {
+				return "", err
+			}
+			firstSystemContent += string(toolJSON)
+			if i < len(tools)-1 {
+				firstSystemContent += ", "
+			}
+		}
+		firstSystemContent += "]"
+	}
+
+	// Output first system block if it has content
+	if firstSystemContent != "" {
+		sb.WriteString("<|im_start|>system\n")
+		sb.WriteString(firstSystemContent)
+		sb.WriteString("<|im_end|>\n")
+	}
+
+	// Find the index of the last assistant message for thinking stripping
+	lastAssistantIndex := -1
+	for i := len(messages) - 1; i >= startIdx; i-- {
+		if messages[i].Role == "assistant" {
+			lastAssistantIndex = i
+			break
+		}
+	}
+
+	// Track whether we need to add generation prompt
+	needsGenerationPrompt := len(messages) > 0
+
+	for i := startIdx; i < len(messages); i++ {
+		message := messages[i]
+		switch message.Role {
+		case "system":
+			// Additional system messages (after the first) are rendered normally
+			sb.WriteString("<|im_start|>system\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+
+		case "user":
+			sb.WriteString("<|im_start|>user\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+
+		case "assistant":
+			sb.WriteString("<|im_start|>assistant\n")
+
+			// Check if this is the last assistant message
+			isLastAssistant := i == lastAssistantIndex
+
+			// Process content (may need thinking stripped)
+			content := message.Content
+
+			// Handle thinking tags in assistant content
+			keepPastThinking := r.IsThinking && (thinkValue != nil && thinkValue.Bool())
+			if strings.Contains(content, "</think>") {
+				parts := strings.SplitN(content, "</think>", 2)
+				if len(parts) > 1 {
+					if !isLastAssistant && !keepPastThinking {
+						// Strip thinking entirely for past assistant messages
+						content = strings.TrimSpace(parts[1])
+					} else {
+						// Preserve thinking but trim whitespace after </think>
+						content = parts[0] + "</think>" + strings.TrimLeft(parts[1], " \t\n\r")
+					}
+				}
+			}
+
+			if len(message.ToolCalls) > 0 {
+				// Assistant with tool calls - write content first (if any after stripping)
+				if content != "" {
+					sb.WriteString(content)
+				}
+
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("<|tool_call_start|>")
+					toolCallJSON := map[string]any{
+						"name":      toolCall.Function.Name,
+						"arguments": toolCall.Function.Arguments,
+					}
+					callJSON, _ := json.Marshal(toolCallJSON)
+					sb.WriteString(string(callJSON))
+					sb.WriteString("<|tool_call_end|>")
+				}
+			} else {
+				sb.WriteString(content)
+			}
+
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true // Always add gen prompt after assistant when add_generation_prompt=true
+
+		case "tool":
+			// Tool responses are rendered as plain messages per the chat template
+			sb.WriteString("<|im_start|>tool\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+		}
+	}
+
+	// Add generation prompt
+	if needsGenerationPrompt {
+		sb.WriteString("<|im_start|>assistant\n")
+		// Note: Model is a "thinking-only" model - it will output <think> itself
+		// We don't add <think> tag to the prompt
+	}
+
+	return sb.String(), nil
+}

model/renderers/lfm2_test.go

@@ -0,0 +1,427 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestLFM2Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple system messages rendered separately",
+			messages: []api.Message{
+				{Role: "system", Content: "First instruction."},
+				{Role: "system", Content: "Second instruction."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nFirst instruction.<|im_end|>\n<|im_start|>system\nSecond instruction.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multi-turn conversation",
+			messages: []api.Message{
+				{Role: "user", Content: "What is 2+2?"},
+				{Role: "assistant", Content: "The answer is 4."},
+				{Role: "user", Content: "Thanks!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat is 2+2?<|im_end|>\n<|im_start|>assistant\nThe answer is 4.<|im_end|>\n<|im_start|>user\nThanks!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "only system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are helpful.<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// When assistant is the LAST assistant, thinking is preserved (even with keep_past_thinking=false)
+			name: "user-assistant-user: last assistant preserves thinking",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reasoning</think>A1"},
+				{Role: "user", Content: "Q2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reasoning</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With two assistants, first is stripped (not last), second preserved (is last)
+			name: "multi-turn thinking: first stripped, second preserved",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\nA1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With thinking enabled (keep_past_thinking=true), both preserved
+			name: "multi-turn thinking: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reason1</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "assistant with tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "assistant with content and tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather in Paris?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather in Paris?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `Let me check.<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tool response",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{Role: "assistant", Content: "Let me check."},
+				{Role: "tool", Content: "22C, Sunny"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<|im_end|>\n<|im_start|>tool\n22C, Sunny<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "Get weather for Paris and London"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "London",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `Get weather for Paris and London<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|tool_call_start|>{"arguments":{"location":"London"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `You are helpful.` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "multiple tools without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+					},
+				},
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_time",
+						Description: "Get time",
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nList of tools: [{\"type\":\"function\",\"function\":{\"name\":\"get_weather\",\"description\":\"Get weather\",\"parameters\":{\"type\":\"\",\"properties\":null}}}, {\"type\":\"function\",\"function\":{\"name\":\"get_time\",\"description\":\"Get time\",\"parameters\":{\"type\":\"\",\"properties\":null}}}]<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "user-tool sequence",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "tool", Content: "22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "full tool call cycle",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "assistant", Content: "Let me check"},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "It's 22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>assistant\nLet me check<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\nIt's 22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "unicode content",
+			messages: []api.Message{
+				{Role: "user", Content: "你好世界! مرحبا 🌍"},
+				{Role: "assistant", Content: "Hello! 👋"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\n你好世界! مرحبا 🌍<|im_end|>\n<|im_start|>assistant\nHello! 👋<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "newlines in content",
+			messages: []api.Message{
+				{Role: "user", Content: "Line 1\nLine 2\n\nLine 4"},
+				{Role: "assistant", Content: "Response with\nmultiple\nlines"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nLine 1\nLine 2\n\nLine 4<|im_end|>\n<|im_start|>assistant\nResponse with\nmultiple\nlines<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "empty assistant content",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: ""},
+				{Role: "user", Content: "OK"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n<|im_end|>\n<|im_start|>user\nOK<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Generation prompt does NOT include <think> - model outputs it
+			name: "generation prompt has no think tag",
+			messages: []api.Message{
+				{Role: "user", Content: "Think hard"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nThink hard<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Interleaved: thinking before tool call - last assistant preserves thinking
+			name: "thinking before tool call (last assistant)",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>I need to check the weather</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>I need to check the weather</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tool calls - first has thinking stripped
+			name: "two assistants with tools: first thinking stripped",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tools - both preserved when thinking enabled
+			name: "two assistants with tools: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>checking</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Content before thinking before tool call
+			name: "content then thinking then tool call",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.<think>Using weather API</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<think>Using weather API</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+	}
+
+	renderer := &LFM2Renderer{IsThinking: true}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatalf("Render() error = %v", err)
+			}
+			if diff := cmp.Diff(tt.expected, rendered); diff != "" {
+				t.Errorf("Render() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}

model/renderers/renderer.go

@@ -82,6 +82,10 @@ func rendererForName(name string) Renderer {
 		return &FunctionGemmaRenderer{}
 	case "glm-4.7":
 		return &GLM47Renderer{}
+	case "lfm2":
+		return &LFM2Renderer{IsThinking: false}
+	case "lfm2-thinking":
+		return &LFM2Renderer{IsThinking: true}
 	default:
 		return nil
 	}

server/quantization.go

@@ -198,8 +198,8 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 	name := t.Name
 	quantize := strings.HasSuffix(name, "weight")
 
-	// don't quantize vision stuff
-	quantize = quantize && (!strings.Contains(name, "v.") || strings.Contains(name, "_v."))
+	// don't quantize vision encoder tensors (named with "v." prefix)
+	quantize = quantize && !strings.HasPrefix(name, "v.")
 	quantize = quantize && !strings.Contains(name, "mm.")
 
 	// quantize only 2D and 3D tensors (experts)
@@ -219,6 +219,9 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 	// NOTE: can't use LLM_TN here because the layer number is not known
 	quantize = quantize && !strings.Contains(name, "ssm_conv1d.weight")
 
+	// do not quantize LFM2's shortconv kernel weights
+	quantize = quantize && !strings.Contains(name, "shortconv.conv.weight")
+
 	// do not quantize RWKV's time_mix_first tensors
 	quantize = quantize && !strings.Contains(name, "time_mix_first.weight")
 	quantize = quantize && !strings.Contains(name, "time_mix_w1.weight")

server/routes.go

@@ -1149,6 +1149,9 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		Capabilities: m.Capabilities(),
 		ModifiedAt:   manifest.fi.ModTime(),
 		Requires:     m.Config.Requires,
+		// Several integrations crash on a nil/omitempty+empty ModelInfo, so by
+		// default we return an empty map.
+		ModelInfo: make(map[string]any),
 	}
 
 	if m.Config.RemoteHost != "" {

x/imagegen/manifest.go

@@ -6,8 +6,9 @@ import (
 	"io"
 	"os"
 	"path/filepath"
-	"runtime"
 	"strings"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 // ManifestLayer represents a layer in the manifest.
@@ -32,31 +33,15 @@ type ModelManifest struct {
 	BlobDir  string
 }
 
-// DefaultBlobDir returns the default blob storage directory.
 func DefaultBlobDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	switch runtime.GOOS {
-	case "darwin":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "linux":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "windows":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	default:
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	}
+	return filepath.Join(envconfig.Models(), "blobs")
 }
 
-// DefaultManifestDir returns the default manifest storage directory.
+// DefaultManifestDir returns the manifest storage directory.
+// Respects OLLAMA_MODELS.
+
 func DefaultManifestDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	return filepath.Join(home, ".ollama", "models", "manifests")
+	return filepath.Join(envconfig.Models(), "manifests")
 }
 
 // LoadManifest loads a manifest for the given model name.

x/imagegen/manifest_test.go

@@ -0,0 +1,26 @@
+package imagegen
+
+import (
+	"path/filepath"
+	"testing"
+)
+
+func TestManifestAndBlobDirsRespectOLLAMAModels(t *testing.T) {
+	modelsDir := filepath.Join(t.TempDir(), "models")
+
+	// Simulate packaged/systemd environment
+	t.Setenv("OLLAMA_MODELS", modelsDir)
+	t.Setenv("HOME", "/usr/share/ollama")
+
+	// Manifest dir must respect OLLAMA_MODELS
+	wantManifest := filepath.Join(modelsDir, "manifests")
+	if got := DefaultManifestDir(); got != wantManifest {
+		t.Fatalf("DefaultManifestDir() = %q, want %q", got, wantManifest)
+	}
+
+	// Blob dir must respect OLLAMA_MODELS
+	wantBlobs := filepath.Join(modelsDir, "blobs")
+	if got := DefaultBlobDir(); got != wantBlobs {
+		t.Fatalf("DefaultBlobDir() = %q, want %q", got, wantBlobs)
+	}
+}