Compare commits

...

10 Commits

Author SHA1 Message Date
ParthSareen
5c3bf414ef close to working 2025-12-08 18:17:56 -08:00
ParthSareen
0a9862a383 wip 2025-12-08 15:20:52 -08:00
ParthSareen
f475cc365a wip 2025-12-08 14:42:18 -08:00
ParthSareen
dd3306d3a0 renderers/parsers: olmo3 instruct 2025-12-08 13:23:11 -08:00
nicole pardal
57c1d7db9a fixed generation issue 2025-12-08 00:35:49 -08:00
nicole pardal
91d6370a62 removed original olmo support 2025-12-01 14:17:46 -08:00
nicole pardal
38a2a6468f removed olmo1 support 2025-12-01 14:14:31 -08:00
nicole pardal
064ec63ddf lint 2025-11-26 20:05:25 -08:00
nicole pardal
fd959fbf7a updated converter 2025-11-26 19:42:34 -08:00
nicole pardal
cfc9729edf olmo model initial 2025-11-25 15:49:09 -08:00
14 changed files with 2044 additions and 0 deletions

148
cmd/testolmo/main.go Normal file
View File

@@ -0,0 +1,148 @@
package main
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/ml"
"github.com/ollama/ollama/model"
"github.com/ollama/ollama/model/input"
_ "github.com/ollama/ollama/model/models" // Register all models
"github.com/ollama/ollama/model/renderers"
"github.com/ollama/ollama/sample"
)
func main() {
modelPath := "/Users/parth/.ollama/models/blobs/sha256-a87e10578f328b087f888ac7bd1018555e26028a1130980f20312b4de3a10d70"
fmt.Println("Loading OLMo model...")
m, err := model.New(modelPath, ml.BackendParams{AllocMemory: true})
if err != nil {
log.Fatal(err)
}
if err := m.Backend().Load(context.Background(), func(f float32) {}); err != nil {
log.Fatal(err)
}
fmt.Println("✅ Model loaded successfully!")
// Initialize the cache
cache := m.Config().Cache
if cache != nil {
// Initialize with reasonable defaults:
// - dtype: F16
// - maxSequences: 1 (single sequence)
// - capacity: 2048 (context length)
// - maxBatch: 512
cache.Init(m.Backend(), ml.DTypeF16, 1, 2048, 512)
fmt.Printf("✅ Cache initialized (type: %T)\n", cache)
}
// Use the olmo3 renderer to format the prompt properly
messages := []api.Message{
{Role: "user", Content: "wagwan"},
}
// prompt := "Question: What is machine learning? Answer:"
prompt, err := renderers.RenderWithRenderer("olmo3", messages, nil, nil)
if err != nil {
log.Fatal(err)
}
// prompt = prompt[:len(prompt)]
// prompt := "Question: What is machine learning? Answer:"
fmt.Printf("\nRendered prompt:\n%s\n", prompt)
tp := m.(model.TextProcessor)
tokens, err := tp.Encode(prompt, false)
if err != nil {
log.Fatal(err)
}
fmt.Printf("Tokens: %v (count: %d)\n", tokens, len(tokens))
// Generate 20 tokens
maxTokens := 20
generated := make([]int32, 0, maxTokens)
// Create sampler (temperature=0 for greedy sampling)
sampler := sample.NewSampler(0, 0, 0, 0, -1, nil)
for i := 0; i < maxTokens; i++ {
// Create a new context for each generation step to avoid memory buildup
ctx := m.Backend().NewContext()
var inputTokens []int32
var positions []int32
if i == 0 {
// First iteration: process all prompt tokens
inputTokens = tokens
positions = make([]int32, len(tokens))
for j := range positions {
positions[j] = int32(j)
}
} else {
// Subsequent iterations: only process the newly generated token
// The last token is at position len(tokens)-1 (its index in the sequence)
inputTokens = []int32{tokens[len(tokens)-1]}
positions = []int32{int32(len(tokens) - 1)}
}
sequences := make([]int, len(inputTokens))
// All tokens belong to sequence 0
inputsTensor := ctx.Input().FromInts(inputTokens, len(inputTokens))
outputs := ctx.Input().FromInts([]int32{int32(len(inputTokens) - 1)}, 1)
batch := input.Batch{
Inputs: inputsTensor,
Positions: positions,
Sequences: sequences,
Outputs: outputs,
}
// Forward pass (model.Forward handles cache.StartForward internally)
logits, err := model.Forward(ctx, m, batch)
if err != nil {
ctx.Close()
log.Fatal(err)
}
logits = logits.Contiguous(ctx)
ctx.Forward(logits).Compute(logits)
logitValues := logits.Floats()
// Sample next token
nextToken, err := sampler.Sample(logitValues)
if err != nil {
ctx.Close()
log.Fatal(err)
}
// Close context before moving to next iteration
ctx.Close()
generated = append(generated, nextToken)
tokens = append(tokens, nextToken)
// Decode and print
decoded, _ := tp.Decode([]int32{nextToken})
fmt.Print(decoded)
// Stop on EOS or <|im_end|>
if nextToken == 2 || nextToken == 1 { // Common EOS tokens
break
}
// Check if we generated <|im_end|> (stop token for chat)
if decoded == "<|im_end|>" {
break
}
}
fmt.Println("\n\n✅ Generation completed!")
fullText, _ := tp.Decode(generated)
fmt.Printf("Generated: %s\n", fullText)
}

View File

@@ -200,6 +200,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
conv = &qwen25VLModel{}
case "Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration":
conv = &qwen3VLModel{}
case "OLMo2ForCausalLM", "Olmo2ForCausalLM", "OLMo3ForCausalLM", "Olmo3ForCausalLM":
conv = &olmoModel{}
case "BertModel":
conv = &bertModel{}
case "CohereForCausalLM":

94
convert/convert_olmo.go Normal file
View File

@@ -0,0 +1,94 @@
package convert
import (
"cmp"
"github.com/ollama/ollama/fs/ggml"
)
type olmoModel struct {
ModelParameters
HiddenSize uint32 `json:"hidden_size"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
RMSNormEPS float32 `json:"rms_norm_eps"`
RopeTheta float32 `json:"rope_theta"`
ClampKQV float32 `json:"f_clamp_kqv"`
SlidingWindow uint32 `json:"sliding_window"`
LayerTypes []string `json:"layer_types"`
}
var _ ModelConverter = (*olmoModel)(nil)
func (p *olmoModel) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "olmo"
kv["olmo.block_count"] = p.NumHiddenLayers
kv["olmo.context_length"] = p.MaxPositionEmbeddings
kv["olmo.embedding_length"] = p.HiddenSize
kv["olmo.feed_forward_length"] = p.IntermediateSize
kv["olmo.attention.head_count"] = p.NumAttentionHeads
kv["olmo.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
if p.RopeTheta > 0 {
kv["olmo.rope.freq_base"] = p.RopeTheta
} else {
kv["olmo.rope.freq_base"] = float32(10000.0)
}
if p.RMSNormEPS > 0 {
kv["olmo.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
}
if p.ClampKQV > 0 {
kv["olmo.attention.clamp_kqv"] = p.ClampKQV
}
if p.SlidingWindow > 0 {
kv["olmo.attention.sliding_window"] = p.SlidingWindow
}
if len(p.LayerTypes) > 0 {
kv["olmo.attention.layer_types"] = p.LayerTypes
}
return kv
}
func (p *olmoModel) Tensors(ts []Tensor) []*ggml.Tensor {
out := make([]*ggml.Tensor, 0, len(ts))
for _, t := range ts {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *olmoModel) Replacements() []string {
return []string{
"lm_head", "output",
"model.embed_tokens", "token_embd",
"model.layers", "blk",
"model.norm", "output_norm",
"self_attn.q_proj", "attn_q",
"self_attn.k_proj", "attn_k",
"self_attn.v_proj", "attn_v",
"self_attn.o_proj", "attn_output",
"self_attn.q_norm", "attn_q_norm",
"self_attn.k_norm", "attn_k_norm",
"post_attention_layernorm", "post_attention_norm",
"post_feedforward_layernorm", "post_ffw_norm",
"mlp.gate_proj", "ffn_gate",
"mlp.down_proj", "ffn_down",
"mlp.up_proj", "ffn_up",
}
}

View File

@@ -252,6 +252,7 @@ func (kv KV) OllamaEngineRequired() bool {
"deepseekocr",
"deepseek2",
"nomic-bert",
"olmo2",
}, kv.Architecture())
}

View File

@@ -13,6 +13,7 @@ import (
_ "github.com/ollama/ollama/model/models/mistral3"
_ "github.com/ollama/ollama/model/models/mllama"
_ "github.com/ollama/ollama/model/models/nomicbert"
_ "github.com/ollama/ollama/model/models/olmo"
_ "github.com/ollama/ollama/model/models/qwen2"
_ "github.com/ollama/ollama/model/models/qwen25vl"
_ "github.com/ollama/ollama/model/models/qwen3"

271
model/models/olmo/model.go Normal file
View File

@@ -0,0 +1,271 @@
package olmo
import (
"cmp"
"math"
"github.com/ollama/ollama/fs"
"github.com/ollama/ollama/kvcache"
"github.com/ollama/ollama/ml"
"github.com/ollama/ollama/ml/nn"
"github.com/ollama/ollama/ml/nn/fast"
"github.com/ollama/ollama/ml/nn/rope"
"github.com/ollama/ollama/model"
"github.com/ollama/ollama/model/input"
)
type Options struct {
hiddenSize, numHeads, numKVHeads int
headDim, ropeDim int
eps, ropeBase, ropeScale float32
clampKQV float32
originalContextLength int
attnFactor float32
slidingWindow int32
slidingWindowPattern []bool // per-layer SWA pattern (true = SWA, false = full attention)
}
type Model struct {
model.Base
model.TextProcessor
TokenEmbedding *nn.Embedding `gguf:"token_embd"`
Layers []Layer `gguf:"blk"`
OutputNorm *nn.RMSNorm `gguf:"output_norm"`
Output *nn.Linear `gguf:"output,alt:token_embd"`
Options
}
func New(c fs.Config) (model.Model, error) {
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")...,
),
}
if c.String("tokenizer.ggml.model") != "gpt2" {
return nil, model.ErrUnsupportedTokenizer
}
var pretokenizers []string
if c.String("tokenizer.ggml.pre") != "default" {
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+",
}
}
processor := model.NewBytePairEncoding(&vocabulary, pretokenizers...)
slidingWindow := int32(c.Uint("attention.sliding_window"))
slidingWindowPattern := c.Bools("attention.sliding_window_pattern")
m := Model{
TextProcessor: processor,
Layers: make([]Layer, c.Uint("block_count")),
Options: Options{
hiddenSize: int(c.Uint("embedding_length")),
numHeads: int(c.Uint("attention.head_count")),
numKVHeads: int(c.Uint("attention.head_count_kv")),
headDim: int(c.Uint("attention.key_length")),
ropeDim: int(c.Uint("rope.dimension_count")),
eps: c.Float("attention.layer_norm_rms_epsilon"),
ropeBase: c.Float("rope.freq_base", 1e4),
ropeScale: c.Float("rope.scaling.factor", 1),
clampKQV: c.Float("attention.clamp_kqv", 0),
originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
attnFactor: c.Float("rope.scaling.attn_factor", 1),
slidingWindow: slidingWindow,
slidingWindowPattern: slidingWindowPattern,
},
}
// OLMo3 uses interleaved sliding window attention (every 4th layer is full attention)
m.Cache = kvcache.NewWrapperCache(
kvcache.NewSWACache(slidingWindow, m.Shift),
kvcache.NewCausalCache(m.Shift),
)
return &m, nil
}
type SelfAttention struct {
Query *nn.Linear `gguf:"attn_q"`
Key *nn.Linear `gguf:"attn_k"`
Value *nn.Linear `gguf:"attn_v"`
Output *nn.Linear `gguf:"attn_output"`
QNorm *nn.RMSNorm `gguf:"attn_q_norm"`
KNorm *nn.RMSNorm `gguf:"attn_k_norm"`
RopeFactors ml.Tensor `gguf:"rope_freqs.weight"`
}
func (o *Options) ropeOptions(factors ml.Tensor, isSWA bool) []func(*rope.Options) {
opts := []func(*rope.Options){
rope.WithFactors(factors),
}
if o.originalContextLength > 0 {
if isSWA {
// For SWA layers, use regular rope with no YaRN scaling
// ext_factor=0.0, attn_factor=1.0 per llama.cpp
opts = append(opts,
rope.WithOriginalContextLength(o.originalContextLength),
rope.WithExtrapolationFactor(0.),
rope.WithAttentionFactor(1.),
)
} else {
// For full attention layers, use YaRN scaling
opts = append(opts,
rope.WithOriginalContextLength(o.originalContextLength),
rope.WithExtrapolationFactor(1.),
rope.WithAttentionFactor(o.attnFactor),
)
}
}
return opts
}
func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tensor, cache kvcache.Cache, opts *Options, isSWA bool) ml.Tensor {
batchSize := hiddenState.Dim(1)
headDim := cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
ropeDim := cmp.Or(opts.ropeDim, headDim)
query := sa.Query.Forward(ctx, hiddenState)
if sa.QNorm != nil {
query = sa.QNorm.Forward(ctx, query, opts.eps)
}
query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
key := sa.Key.Forward(ctx, hiddenState)
if sa.KNorm != nil {
key = sa.KNorm.Forward(ctx, key, opts.eps)
}
key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
value := sa.Value.Forward(ctx, hiddenState)
value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
freqScale := float32(1.0)
if !isSWA {
freqScale = 1. / opts.ropeScale
}
ropeOpts := opts.ropeOptions(sa.RopeFactors, isSWA)
query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, freqScale, ropeOpts...)
key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, freqScale, ropeOpts...)
attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), cache)
attention = attention.Reshape(ctx, headDim*opts.numHeads, batchSize)
return sa.Output.Forward(ctx, attention)
}
func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
ropeDim := cmp.Or(m.ropeDim, m.hiddenSize/m.numHeads)
isSWA := m.isSWALayer(layer)
freqScale := float32(1.0)
if !isSWA {
freqScale = 1. / m.ropeScale
}
ropeOpts := m.Options.ropeOptions(m.Layers[layer].SelfAttention.RopeFactors, isSWA)
return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, freqScale, ropeOpts...), nil
}
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 {
SelfAttention *SelfAttention
PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
MLP *MLP
PostFFWNorm *nn.RMSNorm `gguf:"post_ffw_norm"`
}
func (l *Layer) Forward(ctx ml.Context, hiddenState, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options, isSWA bool) ml.Tensor {
residual := hiddenState
hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positions, cache, opts, isSWA)
if outputs != nil {
hiddenState = hiddenState.Rows(ctx, outputs)
residual = residual.Rows(ctx, outputs)
}
if l.PostAttentionNorm != nil {
hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
}
ffnInput := hiddenState.Add(ctx, residual)
hiddenState = l.MLP.Forward(ctx, ffnInput, opts)
if l.PostFFWNorm != nil {
hiddenState = l.PostFFWNorm.Forward(ctx, hiddenState, opts.eps)
}
return hiddenState.Add(ctx, ffnInput)
}
// isSWALayer returns true if the layer uses sliding window attention.
// Uses the sliding_window_pattern from the model config if available,
// otherwise falls back to the default OLMo3 pattern (every 4th layer is full attention).
func (m *Model) isSWALayer(layerIdx int) bool {
if len(m.slidingWindowPattern) > layerIdx {
return m.slidingWindowPattern[layerIdx]
}
// Fallback: OLMo3 pattern where every 4th layer (indices 3, 7, 11, ...) uses full attention
return (layerIdx+1)%4 != 0
}
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)
isSWA := m.isSWALayer(i)
// Set cache type for interleaved SWA (OLMo3)
if wc, ok := m.Cache.(*kvcache.WrapperCache); ok {
if isSWA {
wc.SetLayerType(0) // SWA cache
} else {
wc.SetLayerType(1) // Causal cache
}
}
var outputs ml.Tensor
if i == len(m.Layers)-1 {
outputs = batch.Outputs
}
hiddenState = layer.Forward(ctx, hiddenState, positions, outputs, m.Cache, &m.Options, isSWA)
}
hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
return m.Output.Forward(ctx, hiddenState), nil
}
func init() {
model.Register("olmo2", New)
}

View File

@@ -0,0 +1,132 @@
package olmo
import (
"context"
"fmt"
"log"
"github.com/ollama/ollama/ml"
"github.com/ollama/ollama/model"
"github.com/ollama/ollama/model/input"
"github.com/ollama/ollama/sample"
)
func main() {
modelPath := "/Users/nicole/models/Olmo-3-7B-Think/olmo-3-7b-think-q8_0.gguf"
fmt.Println("Loading OLMo model...")
m, err := model.New(modelPath, ml.BackendParams{AllocMemory: true})
if err != nil {
log.Fatal(err)
}
if err := m.Backend().Load(context.Background(), func(f float32) {}); err != nil {
log.Fatal(err)
}
fmt.Println("✅ Model loaded successfully!")
// Initialize the cache
cache := m.Config().Cache
if cache != nil {
// Initialize with reasonable defaults:
// - dtype: F16
// - maxSequences: 1 (single sequence)
// - capacity: 2048 (context length)
// - maxBatch: 512
cache.Init(m.Backend(), ml.DTypeF16, 1, 2048, 512)
fmt.Printf("✅ Cache initialized (type: %T)\n", cache)
}
// Test generation
prompt := "Question: What is machine learning? Answer:"
fmt.Printf("\nPrompt: %s\n", prompt)
tp := m.(model.TextProcessor)
tokens, err := tp.Encode(prompt, true)
if err != nil {
log.Fatal(err)
}
fmt.Printf("Tokens: %v (count: %d)\n", tokens, len(tokens))
// Generate 20 tokens
maxTokens := 20
generated := make([]int32, 0, maxTokens)
// Create sampler (temperature=0 for greedy sampling)
sampler := sample.NewSampler(0, 0, 0, 0, -1, nil)
for i := 0; i < maxTokens; i++ {
// Create a new context for each generation step to avoid memory buildup
ctx := m.Backend().NewContext()
var inputTokens []int32
var positions []int32
if i == 0 {
// First iteration: process all prompt tokens
inputTokens = tokens
positions = make([]int32, len(tokens))
for j := range positions {
positions[j] = int32(j)
}
} else {
// Subsequent iterations: only process the newly generated token
// The last token is at position len(tokens)-1 (its index in the sequence)
inputTokens = []int32{tokens[len(tokens)-1]}
positions = []int32{int32(len(tokens) - 1)}
}
sequences := make([]int, len(inputTokens))
// All tokens belong to sequence 0
inputsTensor := ctx.Input().FromInts(inputTokens, len(inputTokens))
outputs := ctx.Input().FromInts([]int32{int32(len(inputTokens) - 1)}, 1)
batch := input.Batch{
Inputs: inputsTensor,
Positions: positions,
Sequences: sequences,
Outputs: outputs,
}
// Forward pass (model.Forward handles cache.StartForward internally)
logits, err := model.Forward(ctx, m, batch)
if err != nil {
ctx.Close()
log.Fatal(err)
}
logits = logits.Contiguous(ctx)
ctx.Forward(logits).Compute(logits)
logitValues := logits.Floats()
// Sample next token
nextToken, err := sampler.Sample(logitValues)
if err != nil {
ctx.Close()
log.Fatal(err)
}
// Close context before moving to next iteration
ctx.Close()
generated = append(generated, nextToken)
tokens = append(tokens, nextToken)
// Decode and print
decoded, _ := tp.Decode([]int32{nextToken})
fmt.Print(decoded)
// Stop on EOS
if nextToken == 2 || nextToken == 1 { // Common EOS tokens
break
}
}
fmt.Println("\n\n✅ Generation completed!")
fullText, _ := tp.Decode(generated)
fmt.Printf("Generated: %s\n", fullText)
}

469
model/parsers/olmo3.go Normal file
View File

@@ -0,0 +1,469 @@
package parsers
import (
"context"
"fmt"
"log/slog"
"regexp"
"strconv"
"strings"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/logutil"
)
type olmo3ParserState int
const (
olmo3StateContent olmo3ParserState = iota
olmo3StateToolCalls
olmo3StateToolCallsDone
)
const (
olmo3FuncCallsOpenTag = "<function_calls>"
olmo3FuncCallsCloseTag = "</function_calls>"
)
type Olmo3Parser struct {
state olmo3ParserState
buffer strings.Builder
}
func (p *Olmo3Parser) HasToolSupport() bool {
return true
}
func (p *Olmo3Parser) HasThinkingSupport() bool {
return false
}
func (p *Olmo3Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
p.state = olmo3StateContent
return tools
}
type olmo3ParserEvent interface {
isOlmo3ParserEvent()
}
type olmo3ParserEventContent struct {
content string
}
type olmo3ParserEventToolCalls struct {
calls []api.ToolCall
}
func (olmo3ParserEventContent) isOlmo3ParserEvent() {}
func (olmo3ParserEventToolCalls) isOlmo3ParserEvent() {}
func (p *Olmo3Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
p.buffer.WriteString(s)
if done {
// Drain any remaining content
bufStr := p.buffer.String()
p.buffer.Reset()
if p.state == olmo3StateContent && len(bufStr) > 0 {
return bufStr, "", nil, nil
}
return "", "", nil, nil
}
events := p.parseEvents()
var contentSb strings.Builder
var allCalls []api.ToolCall
for _, event := range events {
switch event := event.(type) {
case olmo3ParserEventContent:
contentSb.WriteString(event.content)
case olmo3ParserEventToolCalls:
allCalls = append(allCalls, event.calls...)
}
}
return contentSb.String(), "", allCalls, nil
}
func (p *Olmo3Parser) parseEvents() []olmo3ParserEvent {
var all []olmo3ParserEvent
keepLooping := true
for keepLooping {
var events []olmo3ParserEvent
events, keepLooping = p.eat()
if len(events) > 0 {
all = append(all, events...)
}
}
if len(all) > 0 {
slog.Log(context.TODO(), logutil.LevelTrace, "olmo3 events parsed", "events", all, "state", p.state, "buffer", p.buffer.String())
}
return all
}
func (p *Olmo3Parser) eat() ([]olmo3ParserEvent, bool) {
var events []olmo3ParserEvent
bufStr := p.buffer.String()
if bufStr == "" {
return events, false
}
switch p.state {
case olmo3StateContent:
if strings.Contains(bufStr, olmo3FuncCallsOpenTag) {
// Found <function_calls> tag
split := strings.SplitN(bufStr, olmo3FuncCallsOpenTag, 2)
content := split[0]
remaining := split[1]
p.buffer.Reset()
p.buffer.WriteString(remaining)
p.state = olmo3StateToolCalls
if len(content) > 0 {
events = append(events, olmo3ParserEventContent{content: content})
}
return events, true
} else if overlapLen := overlap(bufStr, olmo3FuncCallsOpenTag); overlapLen > 0 {
// Partial <function_calls> tag - withhold ambiguous content
unambiguous := bufStr[:len(bufStr)-overlapLen]
ambiguous := bufStr[len(bufStr)-overlapLen:]
p.buffer.Reset()
p.buffer.WriteString(ambiguous)
if len(unambiguous) > 0 {
events = append(events, olmo3ParserEventContent{content: unambiguous})
}
return events, false
} else {
// Regular content - emit all
p.buffer.Reset()
if len(bufStr) > 0 {
events = append(events, olmo3ParserEventContent{content: bufStr})
}
return events, false
}
case olmo3StateToolCalls:
if strings.Contains(bufStr, olmo3FuncCallsCloseTag) {
// Found </function_calls> tag
split := strings.SplitN(bufStr, olmo3FuncCallsCloseTag, 2)
toolCallsStr := split[0]
remaining := split[1]
p.buffer.Reset()
p.buffer.WriteString(remaining)
p.state = olmo3StateToolCallsDone
// Parse the function calls
calls, err := parseOlmo3FunctionCalls(toolCallsStr)
if err != nil {
slog.Log(context.TODO(), logutil.LevelTrace, "failed to parse olmo3 function calls", "error", err, "content", toolCallsStr)
} else if len(calls) > 0 {
events = append(events, olmo3ParserEventToolCalls{calls: calls})
}
return events, true
} else if overlapLen := overlap(bufStr, olmo3FuncCallsCloseTag); overlapLen > 0 {
// Partial </function_calls> tag - wait for more
return events, false
}
// Still collecting tool calls, wait for close tag
return events, false
case olmo3StateToolCallsDone:
// After tool calls, emit remaining content
p.buffer.Reset()
p.state = olmo3StateContent
if len(bufStr) > 0 {
events = append(events, olmo3ParserEventContent{content: bufStr})
}
return events, false
}
return events, false
}
// parseOlmo3FunctionCalls parses function calls in Python-esque format:
// func_name(arg1="value1", arg2=123)
// Multiple calls are separated by newlines
func parseOlmo3FunctionCalls(s string) ([]api.ToolCall, error) {
var calls []api.ToolCall
s = strings.TrimSpace(s)
if s == "" {
return calls, nil
}
// Split by newlines for multiple function calls
lines := strings.Split(s, "\n")
for _, line := range lines {
line = strings.TrimSpace(line)
if line == "" {
continue
}
call, err := parseOlmo3SingleFunctionCall(line)
if err != nil {
return nil, fmt.Errorf("failed to parse function call %q: %w", line, err)
}
calls = append(calls, call)
}
return calls, nil
}
// Regex to match function call: func_name(args)
var funcCallRegex = regexp.MustCompile(`^(\w+)\((.*)\)$`)
// Regex to match a single argument: key=value
// Value can be: "string", 'string', number, true, false, null, or nested structures
var argRegex = regexp.MustCompile(`^(\w+)=(.+)$`)
func parseOlmo3SingleFunctionCall(s string) (api.ToolCall, error) {
matches := funcCallRegex.FindStringSubmatch(s)
if matches == nil {
return api.ToolCall{}, fmt.Errorf("invalid function call format")
}
funcName := matches[1]
argsStr := matches[2]
args, err := parseOlmo3Arguments(argsStr)
if err != nil {
return api.ToolCall{}, fmt.Errorf("failed to parse arguments: %w", err)
}
return api.ToolCall{
Function: api.ToolCallFunction{
Name: funcName,
Arguments: args,
},
}, nil
}
// parseOlmo3Arguments parses comma-separated key=value pairs
// Handles nested parentheses, brackets, braces, and quoted strings
func parseOlmo3Arguments(s string) (map[string]any, error) {
args := make(map[string]any)
s = strings.TrimSpace(s)
if s == "" {
return args, nil
}
// Split by commas, but respect nested structures and quotes
parts := splitArguments(s)
for _, part := range parts {
part = strings.TrimSpace(part)
if part == "" {
continue
}
// Find the first = sign
eqIdx := strings.Index(part, "=")
if eqIdx == -1 {
return nil, fmt.Errorf("invalid argument format: %s", part)
}
key := strings.TrimSpace(part[:eqIdx])
valueStr := strings.TrimSpace(part[eqIdx+1:])
value, err := parseOlmo3Value(valueStr)
if err != nil {
return nil, fmt.Errorf("failed to parse value for %s: %w", key, err)
}
args[key] = value
}
return args, nil
}
// splitArguments splits arguments by commas, respecting quotes and nested structures
func splitArguments(s string) []string {
var parts []string
var current strings.Builder
depth := 0
inString := false
stringChar := byte(0)
escaped := false
for i := 0; i < len(s); i++ {
c := s[i]
if escaped {
current.WriteByte(c)
escaped = false
continue
}
if c == '\\' && inString {
current.WriteByte(c)
escaped = true
continue
}
if (c == '"' || c == '\'') && !inString {
inString = true
stringChar = c
current.WriteByte(c)
continue
}
if c == stringChar && inString {
inString = false
stringChar = 0
current.WriteByte(c)
continue
}
if !inString {
switch c {
case '(', '[', '{':
depth++
current.WriteByte(c)
case ')', ']', '}':
depth--
current.WriteByte(c)
case ',':
if depth == 0 {
parts = append(parts, current.String())
current.Reset()
continue
}
current.WriteByte(c)
default:
current.WriteByte(c)
}
} else {
current.WriteByte(c)
}
}
if current.Len() > 0 {
parts = append(parts, current.String())
}
return parts
}
// parseOlmo3Value parses a value which can be a string, number, boolean, null, array, or object
func parseOlmo3Value(s string) (any, error) {
s = strings.TrimSpace(s)
// Check for quoted string
if (strings.HasPrefix(s, `"`) && strings.HasSuffix(s, `"`)) ||
(strings.HasPrefix(s, `'`) && strings.HasSuffix(s, `'`)) {
// Remove quotes and unescape
inner := s[1 : len(s)-1]
return unescapeString(inner), nil
}
// Check for boolean
if s == "true" || s == "True" {
return true, nil
}
if s == "false" || s == "False" {
return false, nil
}
// Check for null/None
if s == "null" || s == "None" || s == "nil" {
return nil, nil
}
// Check for number
if i, err := strconv.ParseInt(s, 10, 64); err == nil {
return i, nil
}
if f, err := strconv.ParseFloat(s, 64); err == nil {
return f, nil
}
// Check for array [...]
if strings.HasPrefix(s, "[") && strings.HasSuffix(s, "]") {
return parseOlmo3Array(s[1 : len(s)-1])
}
// Check for object {...}
if strings.HasPrefix(s, "{") && strings.HasSuffix(s, "}") {
return parseOlmo3Object(s[1 : len(s)-1])
}
// Default to string without quotes
return s, nil
}
func parseOlmo3Array(s string) ([]any, error) {
s = strings.TrimSpace(s)
if s == "" {
return []any{}, nil
}
parts := splitArguments(s)
var arr []any
for _, part := range parts {
val, err := parseOlmo3Value(part)
if err != nil {
return nil, err
}
arr = append(arr, val)
}
return arr, nil
}
func parseOlmo3Object(s string) (map[string]any, error) {
s = strings.TrimSpace(s)
if s == "" {
return map[string]any{}, nil
}
// Objects use key: value or "key": value format
obj := make(map[string]any)
parts := splitArguments(s)
for _, part := range parts {
part = strings.TrimSpace(part)
if part == "" {
continue
}
// Find colon separator
colonIdx := strings.Index(part, ":")
if colonIdx == -1 {
return nil, fmt.Errorf("invalid object entry: %s", part)
}
keyStr := strings.TrimSpace(part[:colonIdx])
valueStr := strings.TrimSpace(part[colonIdx+1:])
// Remove quotes from key if present
if (strings.HasPrefix(keyStr, `"`) && strings.HasSuffix(keyStr, `"`)) ||
(strings.HasPrefix(keyStr, `'`) && strings.HasSuffix(keyStr, `'`)) {
keyStr = keyStr[1 : len(keyStr)-1]
}
val, err := parseOlmo3Value(valueStr)
if err != nil {
return nil, fmt.Errorf("failed to parse value for key %s: %w", keyStr, err)
}
obj[keyStr] = val
}
return obj, nil
}
func unescapeString(s string) string {
// Handle common escape sequences
s = strings.ReplaceAll(s, `\\`, "\x00") // Placeholder for backslash
s = strings.ReplaceAll(s, `\"`, `"`)
s = strings.ReplaceAll(s, `\'`, `'`)
s = strings.ReplaceAll(s, `\n`, "\n")
s = strings.ReplaceAll(s, `\t`, "\t")
s = strings.ReplaceAll(s, `\r`, "\r")
s = strings.ReplaceAll(s, "\x00", `\`) // Restore backslash
return s
}

483
model/parsers/olmo3_test.go Normal file
View File

@@ -0,0 +1,483 @@
package parsers
import (
"testing"
"github.com/google/go-cmp/cmp"
"github.com/ollama/ollama/api"
)
func TestOlmo3Parser(t *testing.T) {
tests := []struct {
name string
input string
expectedContent string
expectedThinking string
expectedCalls []api.ToolCall
}{
{
name: "simple content",
input: "Hello, how can I help you?",
expectedContent: "Hello, how can I help you?",
},
{
name: "simple tool call",
input: `<function_calls>get_weather(location="San Francisco")</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "San Francisco"},
},
},
},
},
{
name: "content then tool call",
input: `Let me check the weather.<function_calls>get_weather(location="NYC")</function_calls>`,
expectedContent: "Let me check the weather.",
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "NYC"},
},
},
},
},
{
name: "tool call with multiple arguments",
input: `<function_calls>book_flight(from="SFO", to="NYC", date="2024-01-15")</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "book_flight",
Arguments: map[string]any{
"from": "SFO",
"to": "NYC",
"date": "2024-01-15",
},
},
},
},
},
{
name: "multiple tool calls",
input: `<function_calls>get_weather(location="San Francisco")
get_weather(location="New York")</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "San Francisco"},
},
},
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "New York"},
},
},
},
},
{
name: "tool call with numeric argument",
input: `<function_calls>set_temperature(value=72)</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "set_temperature",
Arguments: map[string]any{"value": int64(72)},
},
},
},
},
{
name: "tool call with float argument",
input: `<function_calls>set_price(amount=19.99)</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "set_price",
Arguments: map[string]any{"amount": 19.99},
},
},
},
},
{
name: "tool call with boolean argument",
input: `<function_calls>toggle_setting(enabled=true)</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "toggle_setting",
Arguments: map[string]any{"enabled": true},
},
},
},
},
{
name: "tool call with null argument",
input: `<function_calls>clear_value(field=null)</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "clear_value",
Arguments: map[string]any{"field": nil},
},
},
},
},
{
name: "tool call with array argument",
input: `<function_calls>process_items(items=["apple", "banana", "cherry"])</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "process_items",
Arguments: map[string]any{"items": []any{"apple", "banana", "cherry"}},
},
},
},
},
{
name: "tool call with dict argument",
input: `<function_calls>update_config(settings={"theme": "dark", "fontSize": 14})</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "update_config",
Arguments: map[string]any{
"settings": map[string]any{
"theme": "dark",
"fontSize": int64(14),
},
},
},
},
},
},
{
name: "tool call with nested dict",
input: `<function_calls>create_request(data={"user": {"name": "John", "age": 30}, "active": true})</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "create_request",
Arguments: map[string]any{
"data": map[string]any{
"user": map[string]any{
"name": "John",
"age": int64(30),
},
"active": true,
},
},
},
},
},
},
{
name: "tool call with no arguments",
input: `<function_calls>get_current_time()</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_current_time",
Arguments: map[string]any{},
},
},
},
},
{
name: "tool call with single quotes",
input: `<function_calls>search(query='hello world')</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "search",
Arguments: map[string]any{"query": "hello world"},
},
},
},
},
{
name: "tool call with escaped quotes",
input: `<function_calls>search(query="say \"hello\"")</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "search",
Arguments: map[string]any{"query": `say "hello"`},
},
},
},
},
{
name: "tool call with mixed argument types",
input: `<function_calls>create_user(name="John", age=30, active=true)</function_calls>`,
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "create_user",
Arguments: map[string]any{
"name": "John",
"age": int64(30),
"active": true,
},
},
},
},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
p := &Olmo3Parser{}
p.Init(nil, nil, nil)
content, thinking, calls, err := p.Add(tt.input, false)
if err != nil {
t.Fatalf("unexpected error: %v", err)
}
// Drain remaining content
finalContent, finalThinking, finalCalls, err := p.Add("", true)
if err != nil {
t.Fatalf("unexpected error on done: %v", err)
}
content += finalContent
thinking += finalThinking
calls = append(calls, finalCalls...)
if diff := cmp.Diff(content, tt.expectedContent); diff != "" {
t.Errorf("content mismatch (-got +want):\n%s", diff)
}
if diff := cmp.Diff(thinking, tt.expectedThinking); diff != "" {
t.Errorf("thinking mismatch (-got +want):\n%s", diff)
}
if diff := cmp.Diff(calls, tt.expectedCalls); diff != "" {
t.Errorf("calls mismatch (-got +want):\n%s", diff)
}
})
}
}
func TestOlmo3Parser_Streaming(t *testing.T) {
tests := []struct {
name string
chunks []string
expectedContent string
expectedCalls []api.ToolCall
}{
{
name: "streaming content",
chunks: []string{"Hello, ", "how ", "can I help?"},
expectedContent: "Hello, how can I help?",
},
{
name: "streaming tool call",
chunks: []string{"<function_", "calls>get_weather", "(location=\"SF\")", "</function_calls>"},
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "SF"},
},
},
},
},
{
name: "streaming content then tool call",
chunks: []string{"Let me check.", "<function_calls>", "get_weather(location=\"NYC\")", "</function_calls>"},
expectedContent: "Let me check.",
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "NYC"},
},
},
},
},
{
name: "tool call tag split across chunks",
chunks: []string{"<func", "tion_calls>test()</function_calls>"},
expectedCalls: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "test",
Arguments: map[string]any{},
},
},
},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
p := &Olmo3Parser{}
p.Init(nil, nil, nil)
var allContent string
var allCalls []api.ToolCall
for _, chunk := range tt.chunks {
content, _, calls, err := p.Add(chunk, false)
if err != nil {
t.Fatalf("unexpected error: %v", err)
}
allContent += content
allCalls = append(allCalls, calls...)
}
// Drain
content, _, calls, err := p.Add("", true)
if err != nil {
t.Fatalf("unexpected error on done: %v", err)
}
allContent += content
allCalls = append(allCalls, calls...)
if diff := cmp.Diff(allContent, tt.expectedContent); diff != "" {
t.Errorf("content mismatch (-got +want):\n%s", diff)
}
if diff := cmp.Diff(allCalls, tt.expectedCalls); diff != "" {
t.Errorf("calls mismatch (-got +want):\n%s", diff)
}
})
}
}
func TestOlmo3Parser_HasToolSupport(t *testing.T) {
p := &Olmo3Parser{}
if !p.HasToolSupport() {
t.Error("expected HasToolSupport to return true")
}
}
func TestOlmo3Parser_HasThinkingSupport(t *testing.T) {
p := &Olmo3Parser{}
if p.HasThinkingSupport() {
t.Error("expected HasThinkingSupport to return false")
}
}
func TestParseOlmo3FunctionCalls(t *testing.T) {
tests := []struct {
name string
input string
expected []api.ToolCall
wantErr bool
}{
{
name: "simple call",
input: `get_weather(location="SF")`,
expected: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "SF"},
},
},
},
},
{
name: "multiple args",
input: `send_email(to="user@example.com", subject="Hello", body="Test message")`,
expected: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "send_email",
Arguments: map[string]any{
"to": "user@example.com",
"subject": "Hello",
"body": "Test message",
},
},
},
},
},
{
name: "multiple calls with newlines",
input: `get_weather(location="SF")
get_time(timezone="PST")`,
expected: []api.ToolCall{
{
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "SF"},
},
},
{
Function: api.ToolCallFunction{
Name: "get_time",
Arguments: map[string]any{"timezone": "PST"},
},
},
},
},
{
name: "empty input",
input: "",
expected: nil,
},
{
name: "whitespace only",
input: " \n ",
expected: nil,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
calls, err := parseOlmo3FunctionCalls(tt.input)
if (err != nil) != tt.wantErr {
t.Errorf("parseOlmo3FunctionCalls() error = %v, wantErr %v", err, tt.wantErr)
return
}
if diff := cmp.Diff(calls, tt.expected); diff != "" {
t.Errorf("calls mismatch (-got +want):\n%s", diff)
}
})
}
}
func TestParseOlmo3Value(t *testing.T) {
tests := []struct {
name string
input string
expected any
}{
{"string double quotes", `"hello"`, "hello"},
{"string single quotes", `'hello'`, "hello"},
{"integer", "42", int64(42)},
{"negative integer", "-10", int64(-10)},
{"float", "3.14", 3.14},
{"boolean true", "true", true},
{"boolean True", "True", true},
{"boolean false", "false", false},
{"null", "null", nil},
{"None", "None", nil},
{"empty array", "[]", []any{}},
{"array with strings", `["a", "b"]`, []any{"a", "b"}},
{"array with numbers", "[1, 2, 3]", []any{int64(1), int64(2), int64(3)}},
{"empty object", "{}", map[string]any{}},
{"simple object", `{"name": "John"}`, map[string]any{"name": "John"}},
{"object with number", `{"age": 30}`, map[string]any{"age": int64(30)}},
{"object with multiple keys", `{"a": 1, "b": 2}`, map[string]any{"a": int64(1), "b": int64(2)}},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result, err := parseOlmo3Value(tt.input)
if err != nil {
t.Fatalf("unexpected error: %v", err)
}
if diff := cmp.Diff(result, tt.expected); diff != "" {
t.Errorf("value mismatch (-got +want):\n%s", diff)
}
})
}
}

View File

@@ -54,6 +54,8 @@ func ParserForName(name string) Parser {
return harmony.NewHarmonyMessageHandler()
case "cogito":
return &CogitoParser{}
case "olmo3":
return &Olmo3Parser{}
default:
return nil
}

147
model/renderers/olmo3.go Normal file
View File

@@ -0,0 +1,147 @@
package renderers
import (
"encoding/json"
"fmt"
"sort"
"strings"
"github.com/ollama/ollama/api"
)
const (
olmo3DefaultSystemMessage = "You are a helpful function-calling AI assistant. "
olmo3NoFunctionsMessage = "You do not currently have access to any functions. "
olmo3WithFunctionsMessage = "You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions."
)
type Olmo3Renderer struct{}
func (r *Olmo3Renderer) Render(messages []api.Message, tools []api.Tool, _ *api.ThinkValue) (string, error) {
var sb strings.Builder
var systemMessage *api.Message
filteredMessages := make([]api.Message, 0, len(messages))
for i, message := range messages {
if message.Role == "system" {
if systemMessage == nil {
systemMessage = &messages[i]
}
continue
}
filteredMessages = append(filteredMessages, message)
}
// Render system message
if systemMessage != nil {
// Custom system message - single newline after "system"
sb.WriteString("<|im_start|>system\n")
sb.WriteString(systemMessage.Content)
if len(tools) > 0 {
functionsJSON, err := marshalWithSpaces(tools)
if err != nil {
return "", err
}
sb.WriteString("<functions>")
sb.WriteString(string(functionsJSON))
sb.WriteString("</functions>")
}
sb.WriteString("<|im_end|>\n")
} else {
// Default system message - single newline after "system"
sb.WriteString("<|im_start|>system\n")
sb.WriteString(olmo3DefaultSystemMessage)
if len(tools) > 0 {
functionsJSON, err := marshalWithSpaces(tools)
if err != nil {
return "", err
}
sb.WriteString(olmo3WithFunctionsMessage)
sb.WriteString("<functions>")
sb.WriteString(string(functionsJSON))
sb.WriteString("</functions>")
} else {
sb.WriteString(olmo3NoFunctionsMessage)
sb.WriteString("<functions></functions>")
}
sb.WriteString("<|im_end|>\n")
}
for i, message := range filteredMessages {
lastMessage := i == len(filteredMessages)-1
switch message.Role {
case "user":
sb.WriteString("<|im_start|>user\n")
sb.WriteString(message.Content)
sb.WriteString("<|im_end|>\n")
case "assistant":
sb.WriteString("<|im_start|>assistant\n")
if message.Content != "" {
sb.WriteString(message.Content)
}
if len(message.ToolCalls) > 0 {
sb.WriteString("<function_calls>")
for j, tc := range message.ToolCalls {
// Format as function_name(arg1="value1", arg2="value2")
sb.WriteString(tc.Function.Name)
sb.WriteString("(")
// Get sorted keys for deterministic output
keys := make([]string, 0, len(tc.Function.Arguments))
for k := range tc.Function.Arguments {
keys = append(keys, k)
}
sort.Strings(keys)
for k, key := range keys {
if k > 0 {
sb.WriteString(", ")
}
value, err := json.Marshal(tc.Function.Arguments[key])
if err != nil {
return "", err
}
sb.WriteString(fmt.Sprintf("%s=%s", key, string(value)))
}
sb.WriteString(")")
if j < len(message.ToolCalls)-1 {
sb.WriteString("\n")
}
}
sb.WriteString("</function_calls>")
}
// Add end tag unless it's the last message with content only (prefill)
if !lastMessage || len(message.ToolCalls) > 0 {
sb.WriteString("<|im_end|>\n")
}
case "tool":
sb.WriteString("<|im_start|>environment\n")
sb.WriteString(message.Content)
sb.WriteString("<|im_end|>\n")
}
}
// Add generation prompt if needed
needsGenerationPrompt := true
if len(filteredMessages) > 0 {
lastMsg := filteredMessages[len(filteredMessages)-1]
if lastMsg.Role == "assistant" && len(lastMsg.ToolCalls) == 0 && lastMsg.Content != "" {
needsGenerationPrompt = false
}
}
if needsGenerationPrompt {
sb.WriteString("<|im_start|>assistant\n")
}
return sb.String(), nil
}

View File

@@ -0,0 +1,290 @@
package renderers
import (
"testing"
"github.com/google/go-cmp/cmp"
"github.com/ollama/ollama/api"
)
func TestOlmo3Renderer(t *testing.T) {
tests := []struct {
name string
msgs []api.Message
tools []api.Tool
expected string
}{
{
name: "basic without system - adds default system",
msgs: []api.Message{
{Role: "user", Content: "Hello!"},
},
expected: "<|im_start|>system\n" +
"You are a helpful function-calling AI assistant. You do not currently have access to any functions. <functions></functions><|im_end|>\n" +
"<|im_start|>user\n" +
"Hello!<|im_end|>\n" +
"<|im_start|>assistant\n\n",
},
{
name: "with system message no tools",
msgs: []api.Message{
{Role: "system", Content: "You are a helpful assistant."},
{Role: "user", Content: "Hello!"},
},
expected: "<|im_start|>system\n" +
"You are a helpful assistant.<|im_end|>\n" +
"<|im_start|>user\n" +
"Hello!<|im_end|>\n" +
"<|im_start|>assistant\n\n",
},
{
name: "with system message and tools",
msgs: []api.Message{
{Role: "system", Content: "You are a helpful assistant."},
{Role: "user", Content: "What is the weather?"},
},
tools: []api.Tool{
{
Type: "function",
Function: api.ToolFunction{
Name: "get_weather",
Description: "Get the current weather",
Parameters: api.ToolFunctionParameters{
Type: "object",
Required: []string{"location"},
Properties: map[string]api.ToolProperty{
"location": {Type: api.PropertyType{"string"}, Description: "The city"},
},
},
},
},
},
expected: "<|im_start|>system\n" +
`You are a helpful assistant.<functions>[{"type": "function", "function": {"name": "get_weather", "description": "Get the current weather", "parameters": {"type": "object", "required": ["location"], "properties": {"location": {"type": "string", "description": "The city"}}}}}]</functions><|im_end|>` + "\n" +
"<|im_start|>user\n" +
"What is the weather?<|im_end|>\n" +
"<|im_start|>assistant\n\n",
},
{
name: "default system with tools - includes function instruction",
msgs: []api.Message{
{Role: "user", Content: "What is the weather?"},
},
tools: []api.Tool{
{
Type: "function",
Function: api.ToolFunction{
Name: "get_weather",
Description: "Get the current weather",
Parameters: api.ToolFunctionParameters{
Type: "object",
Required: []string{"location"},
Properties: map[string]api.ToolProperty{
"location": {Type: api.PropertyType{"string"}, Description: "The city"},
},
},
},
},
},
expected: "<|im_start|>system\n" +
"You are a helpful function-calling AI assistant. " +
"You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions." +
`<functions>[{"type": "function", "function": {"name": "get_weather", "description": "Get the current weather", "parameters": {"type": "object", "required": ["location"], "properties": {"location": {"type": "string", "description": "The city"}}}}}]</functions><|im_end|>` + "\n" +
"<|im_start|>user\n" +
"What is the weather?<|im_end|>\n" +
"<|im_start|>assistant\n\n",
},
{
name: "assistant with tool calls - function call syntax",
msgs: []api.Message{
{Role: "system", Content: "You are a helpful assistant."},
{Role: "user", Content: "What is the weather in SF?"},
{
Role: "assistant",
Content: "Let me check the weather.",
ToolCalls: []api.ToolCall{
{
ID: "call_1",
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{
"location": "San Francisco",
},
},
},
},
},
{Role: "tool", Content: `{"temperature": 68}`, ToolName: "get_weather"},
},
tools: []api.Tool{
{
Type: "function",
Function: api.ToolFunction{
Name: "get_weather",
Description: "Get the current weather",
Parameters: api.ToolFunctionParameters{
Type: "object",
Required: []string{"location"},
Properties: map[string]api.ToolProperty{
"location": {Type: api.PropertyType{"string"}, Description: "The city"},
},
},
},
},
},
expected: "<|im_start|>system\n" +
`You are a helpful assistant.<functions>[{"type": "function", "function": {"name": "get_weather", "description": "Get the current weather", "parameters": {"type": "object", "required": ["location"], "properties": {"location": {"type": "string", "description": "The city"}}}}}]</functions><|im_end|>` + "\n" +
"<|im_start|>user\n" +
"What is the weather in SF?<|im_end|>\n" +
"<|im_start|>assistant\n" +
`Let me check the weather.<function_calls>get_weather(location="San Francisco")</function_calls><|im_end|>` + "\n" +
"<|im_start|>environment\n" +
`{"temperature": 68}<|im_end|>` + "\n" +
"<|im_start|>assistant\n\n",
},
{
name: "multi-turn conversation",
msgs: []api.Message{
{Role: "system", Content: "You are a helpful assistant."},
{Role: "user", Content: "Hello"},
{Role: "assistant", Content: "Hi there!"},
{Role: "user", Content: "How are you?"},
},
expected: "<|im_start|>system\n" +
"You are a helpful assistant.<|im_end|>\n" +
"<|im_start|>user\n" +
"Hello<|im_end|>\n" +
"<|im_start|>assistant\n" +
"Hi there!<|im_end|>\n" +
"<|im_start|>user\n" +
"How are you?<|im_end|>\n" +
"<|im_start|>assistant\n\n",
},
{
name: "parallel tool calls - newline separated",
msgs: []api.Message{
{Role: "user", Content: "Get weather in SF and NYC"},
{
Role: "assistant",
ToolCalls: []api.ToolCall{
{
ID: "call_1",
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "San Francisco"},
},
},
{
ID: "call_2",
Function: api.ToolCallFunction{
Name: "get_weather",
Arguments: map[string]any{"location": "New York"},
},
},
},
},
{Role: "tool", Content: `{"temperature": 68}`, ToolName: "get_weather"},
{Role: "tool", Content: `{"temperature": 55}`, ToolName: "get_weather"},
},
tools: []api.Tool{
{
Type: "function",
Function: api.ToolFunction{
Name: "get_weather",
Parameters: api.ToolFunctionParameters{
Type: "object",
Properties: map[string]api.ToolProperty{
"location": {Type: api.PropertyType{"string"}},
},
},
},
},
},
expected: "<|im_start|>system\n" +
"You are a helpful function-calling AI assistant. " +
"You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions." +
`<functions>[{"type": "function", "function": {"name": "get_weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}}}}]</functions><|im_end|>` + "\n" +
"<|im_start|>user\n" +
"Get weather in SF and NYC<|im_end|>\n" +
"<|im_start|>assistant\n" +
`<function_calls>get_weather(location="San Francisco")` + "\n" +
`get_weather(location="New York")</function_calls><|im_end|>` + "\n" +
"<|im_start|>environment\n" +
`{"temperature": 68}<|im_end|>` + "\n" +
"<|im_start|>environment\n" +
`{"temperature": 55}<|im_end|>` + "\n" +
"<|im_start|>assistant\n\n",
},
{
name: "tool call with multiple arguments",
msgs: []api.Message{
{Role: "user", Content: "Book a flight"},
{
Role: "assistant",
ToolCalls: []api.ToolCall{
{
ID: "call_1",
Function: api.ToolCallFunction{
Name: "book_flight",
Arguments: map[string]any{
"from": "SFO",
"to": "NYC",
},
},
},
},
},
},
tools: []api.Tool{
{
Type: "function",
Function: api.ToolFunction{
Name: "book_flight",
Parameters: api.ToolFunctionParameters{
Type: "object",
Properties: map[string]api.ToolProperty{
"from": {Type: api.PropertyType{"string"}},
"to": {Type: api.PropertyType{"string"}},
},
},
},
},
},
expected: "<|im_start|>system\n" +
"You are a helpful function-calling AI assistant. " +
"You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions." +
`<functions>[{"type": "function", "function": {"name": "book_flight", "parameters": {"type": "object", "properties": {"from": {"type": "string"}, "to": {"type": "string"}}}}}]</functions><|im_end|>` + "\n" +
"<|im_start|>user\n" +
"Book a flight<|im_end|>\n" +
"<|im_start|>assistant\n" +
`<function_calls>book_flight(from="SFO", to="NYC")</function_calls><|im_end|>` + "\n" +
"<|im_start|>assistant\n\n",
},
{
name: "assistant prefill - no generation prompt",
msgs: []api.Message{
{Role: "user", Content: "Hello"},
{Role: "assistant", Content: "Hi there!"},
},
expected: "<|im_start|>system\n" +
"You are a helpful function-calling AI assistant. You do not currently have access to any functions. <functions></functions><|im_end|>\n" +
"<|im_start|>user\n" +
"Hello<|im_end|>\n" +
"<|im_start|>assistant\n" +
"Hi there!",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
rendered, err := (&Olmo3Renderer{}).Render(tt.msgs, tt.tools, nil)
if err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(rendered, tt.expected); diff != "" {
t.Errorf("mismatch (-got +want):\n%s", diff)
}
})
}
}

View File

@@ -59,6 +59,9 @@ func rendererForName(name string) Renderer {
case "cogito":
renderer := &CogitoRenderer{isThinking: true}
return renderer
case "olmo3":
renderer := &Olmo3Renderer{}
return renderer
default:
return nil
}

View File

@@ -110,6 +110,7 @@ func renderPrompt(m *Model, msgs []api.Message, tools []api.Tool, think *api.Thi
if err != nil {
return "", err
}
slog.Debug("rendered prompt", "renderer", m.Config.Renderer, "prompt", rendered)
return rendered, nil
}