fixed converter

convert/convert.go

@@ -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":

convert/convert_olmo.go

@@ -0,0 +1,124 @@
+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type ropeScaling struct {
+	Factor                    float32 `json:"factor"`
+	OriginalMaxPositionEmbeds uint32  `json:"original_max_position_embeddings"`
+	AttentionFactor           float32 `json:"attention_factor"`
+	BetaFast                  float32 `json:"beta_fast"`
+	BetaSlow                  float32 `json:"beta_slow"`
+	RopeType                  string  `json:"rope_type"`
+	ExtrapolationFactor       float32 `json:"extrapolation_factor"`
+}
+
+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"`
+	RopeScaling           *ropeScaling `json:"rope_scaling"`
+	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"] = "olmo2"
+	kv["olmo2.block_count"] = p.NumHiddenLayers
+	kv["olmo2.context_length"] = p.MaxPositionEmbeddings
+	kv["olmo2.embedding_length"] = p.HiddenSize
+	kv["olmo2.feed_forward_length"] = p.IntermediateSize
+	kv["olmo2.attention.head_count"] = p.NumAttentionHeads
+	kv["olmo2.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
+
+	if p.RopeTheta > 0 {
+		kv["olmo2.rope.freq_base"] = p.RopeTheta
+	} else {
+		kv["olmo2.rope.freq_base"] = float32(10000.0)
+	}
+
+	if p.RopeScaling != nil {
+		if p.RopeScaling.Factor > 0 {
+			kv["olmo2.rope.scaling.factor"] = p.RopeScaling.Factor
+		}
+		if p.RopeScaling.OriginalMaxPositionEmbeds > 0 {
+			kv["olmo2.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeds
+		}
+		if p.RopeScaling.AttentionFactor > 0 {
+			kv["olmo2.rope.scaling.attn_factor"] = p.RopeScaling.AttentionFactor
+		}
+		if p.RopeScaling.RopeType != "" {
+			kv["olmo2.rope.scaling.type"] = p.RopeScaling.RopeType
+		}
+	}
+
+	if p.RMSNormEPS > 0 {
+		kv["olmo2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	}
+
+	if p.ClampKQV > 0 {
+		kv["olmo2.attention.clamp_kqv"] = p.ClampKQV
+	}
+
+	if p.SlidingWindow > 0 {
+		kv["olmo2.attention.sliding_window"] = p.SlidingWindow
+	}
+
+	if len(p.LayerTypes) > 0 {
+		slidingPattern := make([]bool, len(p.LayerTypes))
+		for i, layerType := range p.LayerTypes {
+			slidingPattern[i] = (layerType == "sliding_attention")
+		}
+		kv["olmo2.attention.sliding_window_pattern"] = slidingPattern
+	}
+
+	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",
+	}
+}

fs/ggml/ggml.go

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

model/models/models.go

@@ -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"

model/models/olmo/model.go

@@ -0,0 +1,298 @@
+package olmo
+
+import (
+	"fmt"
+	"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/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+const (
+	cacheTypeSWA = iota
+	cacheTypeCausal
+)
+
+type Options struct {
+	hiddenSize, numHeads, numKVHeads int
+	// headDim, ropeDim                 int
+	eps, ropeBase, ropeScale float32
+
+	originalContextLength int
+	attnFactor            float32
+
+	ropeType          string
+	ropeExtrapolation float32
+
+	ropeBetaFast float32
+	ropeBetaSlow float32
+
+	slidingWindowPattern []bool
+}
+
+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", false),
+		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...)
+
+	hiddenSize := int(c.Uint("embedding_length"))
+	numHeads := int(c.Uint("attention.head_count"))
+	numKVHeads := int(c.Uint("attention.head_count_kv"))
+	eps := c.Float("attention.layer_norm_rms_epsilon")
+	ropeBase := c.Float("rope.freq_base", 1e4)
+	ropeScale := c.Float("rope.scaling.factor", 1)
+	originalContextLength := int(c.Uint("rope.scaling.original_context_length"))
+	attnFactor := c.Float("rope.scaling.attn_factor", 1)
+	ropeType := c.String("rope.scaling.type")
+	ropeExtrapolation := c.Float("rope.scaling.extrapolation_factor", 1.0)
+
+	fmt.Printf("hiddenSize: %d\n", hiddenSize)
+	fmt.Printf("numHeads: %d\n", numHeads)
+	fmt.Printf("numKVHeads: %d\n", numKVHeads)
+	fmt.Printf("eps: %f\n", eps)
+	fmt.Printf("ropeBase: %f\n", ropeBase)
+	fmt.Printf("ropeScale: %f\n", ropeScale)
+	fmt.Printf("originalContextLength: %d\n", originalContextLength)
+	fmt.Printf("attnFactor: %f\n", attnFactor)
+	fmt.Printf("ropeType: %s\n", ropeType)
+	fmt.Printf("ropeExtrapolation: %f\n", ropeExtrapolation)
+	fmt.Printf("sliding_window_pattern: %v\n", c.Bools("attention.sliding_window_pattern"))
+
+	m := Model{
+		TextProcessor: processor,
+		Layers:        make([]Layer, c.Uint("block_count")),
+		Options: Options{
+			hiddenSize:            hiddenSize,
+			numHeads:              numHeads,
+			numKVHeads:            numKVHeads,
+			eps:                   eps,
+			ropeBase:              ropeBase,
+			ropeScale:             ropeScale,
+			originalContextLength: originalContextLength,
+			attnFactor:            attnFactor,
+			ropeType:              ropeType,
+			ropeExtrapolation:     ropeExtrapolation,
+			ropeBetaFast:          32.0,
+			ropeBetaSlow:          1.0,
+			slidingWindowPattern:  c.Bools("attention.sliding_window_pattern"),
+		},
+	}
+
+	m.Cache = kvcache.NewWrapperCache(
+		kvcache.NewSWACache(int32(c.Uint("attention.sliding_window")), 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 (m *Model) applyRoPE(ctx ml.Context, states, positions ml.Tensor, ropeDim int, isSWA bool) ml.Tensor {
+
+	var ropeOpts []func(*rope.Options)
+
+	ropeOpts = append(ropeOpts, rope.WithTypeNeoX())
+
+	// Both SWA and non-SWA use beta_fast and beta_slow
+	// defaults
+	ropeOpts = append(ropeOpts,
+		rope.WithBetaFast(m.ropeBetaFast),
+		rope.WithBetaSlow(m.ropeBetaSlow),
+	)
+
+	// SWA uses freq_scale=1.0, ext_factor=0.0, attn_factor=1.0
+	// Non-SWA uses full yarn parameters
+	if m.originalContextLength > 0 {
+		ropeOpts = append(ropeOpts,
+			rope.WithOriginalContextLength(m.originalContextLength),
+		)
+
+		// no yarn for swa
+		if isSWA {
+			ropeOpts = append(ropeOpts,
+				rope.WithExtrapolationFactor(0),
+				rope.WithAttentionFactor(1.),
+			)
+		} else {
+			ropeOpts = append(ropeOpts,
+				rope.WithExtrapolationFactor(m.ropeExtrapolation),
+				rope.WithAttentionFactor(m.attnFactor),
+			)
+		}
+	}
+
+	freqScale := float32(1.0)
+	if !isSWA {
+		freqScale = 1. / m.ropeScale
+	}
+
+	return nn.RoPE(ctx, states, positions, ropeDim, m.ropeBase, freqScale, ropeOpts...)
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tensor, cache kvcache.Cache, m *Model, isSWA bool) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	headDim := m.hiddenSize / m.numHeads
+	ropeDim := headDim
+
+	query := sa.Query.Forward(ctx, hiddenState)
+	// double check type
+	query = sa.QNorm.Forward(ctx, query, m.eps)
+	query = query.Reshape(ctx, headDim, m.numHeads, batchSize)
+	//check here
+
+	query = m.applyRoPE(ctx, query, positions, ropeDim, isSWA)
+
+	key := sa.Key.Forward(ctx, hiddenState)
+	key = sa.KNorm.Forward(ctx, key, m.eps)
+	key = key.Reshape(ctx, headDim, m.numKVHeads, batchSize)
+	key = m.applyRoPE(ctx, key, positions, ropeDim, isSWA)
+
+	value := sa.Value.Forward(ctx, hiddenState)
+	value = value.Reshape(ctx, headDim, m.numKVHeads, batchSize)
+
+	// check attention scaling as well
+	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), cache)
+	attention = attention.Reshape(ctx, m.hiddenSize, batchSize)
+
+	return sa.Output.Forward(ctx, attention)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	ropeDim := m.hiddenSize / m.numHeads
+	isSWA := m.isSWALayer(layer)
+	return m.applyRoPE(ctx, key, shift, ropeDim, isSWA), 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, m *Model) 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, m *Model, isSWA bool) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positions, cache, m, isSWA)
+
+	// return hiddenState
+
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+	// i think this should be after getting the rows?
+	hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, m.eps)
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLP.Forward(ctx, hiddenState, m)
+	hiddenState = l.PostFFWNorm.Forward(ctx, hiddenState, m.eps)
+
+	return hiddenState.Add(ctx, residual)
+}
+
+// Olmo3 has Sliding Window Attention (SWA) 3 out of 4 layers.
+func (m *Model) isSWALayer(layerIdx int) bool {
+	return m.Options.slidingWindowPattern[layerIdx]
+}
+
+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)
+		cacheType := cacheTypeSWA
+
+		isSWA := m.isSWALayer(i)
+		if !isSWA {
+			cacheType = cacheTypeCausal
+		}
+
+		wc := m.Cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(cacheType)
+		// would need to check the cache at the layer instead
+		if causal, ok := wc.UnderlyingCache().(*kvcache.Causal); ok {
+			// TODO: not sure about the index here
+			causal.SetCausal(ctx, kvcache.CausalOptions{Except: []int{}})
+		}
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenState = layer.Forward(ctx, hiddenState, positions, outputs, m.Cache, m, isSWA)
+
+		// return hiddenState, nil
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState), nil
+}
+
+func init() {
+	model.Register("olmo2", New)
+}

model/models/olmo/olmo_test.go

@@ -0,0 +1,568 @@
+package olmo
+
+import (
+	"encoding/binary"
+	"encoding/json"
+	"flag"
+	"fmt"
+	"log/slog"
+	"math"
+	"os"
+	"path/filepath"
+	"strings"
+	"testing"
+
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/logutil"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+	typemodel "github.com/ollama/ollama/types/model"
+)
+
+var args struct {
+	model,
+	prompt string
+	layers int
+}
+
+func TestMain(m *testing.M) {
+	flag.StringVar(&args.model, "model", "", "path to model (e.g., olmo3:latest)")
+	flag.StringVar(&args.prompt, "prompt", "Hello, how are", "model prompt")
+	flag.IntVar(&args.layers, "layers", math.MaxInt, "num of gpu layers")
+	flag.Parse()
+
+	slog.SetDefault(logutil.NewLogger(os.Stderr, envconfig.LogLevel()))
+
+	os.Exit(m.Run())
+}
+
+func blob(tb testing.TB, modelName string) string {
+	tb.Helper()
+
+	models := envconfig.Models()
+	manifest, err := os.Open(filepath.Join(models, "manifests", typemodel.ParseName(modelName).Filepath()))
+	if err != nil {
+		tb.Fatal(err)
+	}
+	defer manifest.Close()
+
+	var m struct {
+		Layers []struct {
+			MediaType string `json:"mediaType"`
+			Digest    string `json:"digest"`
+		} `json:"layers"`
+	}
+
+	if err := json.NewDecoder(manifest).Decode(&m); err != nil {
+		tb.Fatal(err)
+	}
+
+	for _, layer := range m.Layers {
+		if layer.MediaType == "application/vnd.ollama.image.model" {
+			tb.Log("using model blob", layer.Digest)
+			return filepath.Join(models, "blobs", strings.ReplaceAll(layer.Digest, ":", "-"))
+		}
+	}
+
+	tb.Fatal("model blob not found")
+	return ""
+}
+
+func loadFloatsFromBinary(filename string) ([]float32, error) {
+	f, err := os.Open(filename)
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	fi, err := f.Stat()
+	if err != nil {
+		return nil, err
+	}
+	if fi.Size()%4 != 0 {
+		return nil, fmt.Errorf("file size %d not multiple of 4", fi.Size())
+	}
+
+	n := int(fi.Size() / 4)
+	floats := make([]float32, n)
+	if err := binary.Read(f, binary.LittleEndian, floats); err != nil {
+		return nil, err
+	}
+	return floats, nil
+}
+
+func TestTokenization(t *testing.T) {
+	if args.model == "" {
+		t.Skip("no model specified, use -model flag")
+	}
+
+	m, err := model.New(blob(t, args.model), ml.BackendParams{AllocMemory: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if err := m.Backend().Load(t.Context(), func(float32) {}); err != nil {
+		t.Fatal(err)
+	}
+
+	prompt := args.prompt
+	if prompt == "" {
+		prompt = "hello, how are you?"
+	}
+
+	tp := m.(model.TextProcessor)
+	tokens, err := tp.Encode(prompt, false)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	t.Logf("prompt: %q", prompt)
+	t.Logf("tokens: %v", tokens)
+	t.Logf("num tokens: %d", len(tokens))
+
+	decoded, err := tp.Decode(tokens)
+	if err != nil {
+		t.Fatal(err)
+	}
+	t.Logf("decoded: %q", decoded)
+}
+
+func TestAttentionForward(t *testing.T) {
+	if args.model == "" {
+		t.Skip("no model specified, use -model flag")
+	}
+
+	m, err := model.New(blob(t, args.model), ml.BackendParams{AllocMemory: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if err := m.Backend().Load(t.Context(), func(float32) {}); err != nil {
+		t.Fatal(err)
+	}
+
+	olmoModel := m.(*Model)
+	t.Logf("Model options: hiddenSize=%d, numHeads=%d, numKVHeads=%d",
+		olmoModel.hiddenSize, olmoModel.numHeads, olmoModel.numKVHeads)
+	t.Logf("Layer 0 attention: %+v", olmoModel.Layers[0].SelfAttention)
+
+	ctx := m.Backend().NewContext()
+
+	// Create test hidden states: (hiddenSize, batchSize)
+	batchSize := 4
+	hiddenSize := olmoModel.hiddenSize
+	hsFloats := make([]float32, hiddenSize*batchSize)
+	for i := range hsFloats {
+		hsFloats[i] = float32(i%100) / 100.0 // Simple test values
+	}
+
+	hiddenStates := ctx.Input().FromFloats(hsFloats, hiddenSize, batchSize)
+	t.Logf("hiddenStates shape: %v", hiddenStates.Shape())
+
+	positions := ctx.Input().FromInts([]int32{0, 1, 2, 3}, batchSize)
+
+	// Test attention forward (without cache for simplicity)
+	attentionBlock := olmoModel.Layers[0].SelfAttention
+	isSWA := olmoModel.isSWALayer(0)
+	t.Logf("Layer 0 isSWA: %v", isSWA)
+
+	result := attentionBlock.Forward(ctx, hiddenStates, positions, nil, olmoModel, isSWA)
+	result = result.Contiguous(ctx)
+	ctx.Forward(result).Compute(result)
+
+	t.Logf("Attention result shape: %v dtype: %v", result.Shape(), result.DType())
+
+	// Optionally dump to file
+	// if err := os.WriteFile("/tmp/olmo_attention_output.bin", result.Bytes(), 0644); err != nil {
+	// 	t.Fatal(err)
+	// }
+}
+
+func TestMLPForward(t *testing.T) {
+	if args.model == "" {
+		t.Skip("no model specified, use -model flag")
+	}
+
+	m, err := model.New(blob(t, args.model), ml.BackendParams{AllocMemory: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if err := m.Backend().Load(t.Context(), func(float32) {}); err != nil {
+		t.Fatal(err)
+	}
+
+	olmoModel := m.(*Model)
+	ctx := m.Backend().NewContext()
+
+	// Create test hidden states
+	batchSize := 4
+	hiddenSize := olmoModel.hiddenSize
+	hsFloats := make([]float32, hiddenSize*batchSize)
+	for i := range hsFloats {
+		hsFloats[i] = float32(i%100) / 100.0
+	}
+
+	hiddenStates := ctx.Input().FromFloats(hsFloats, hiddenSize, batchSize)
+	t.Logf("hiddenStates shape: %v", hiddenStates.Shape())
+
+	mlpBlock := olmoModel.Layers[0].MLP
+	result := mlpBlock.Forward(ctx, hiddenStates, olmoModel)
+	result = result.Contiguous(ctx)
+	ctx.Forward(result).Compute(result)
+
+	t.Logf("MLP result shape: %v dtype: %v", result.Shape(), result.DType())
+
+	// Parse result bytes to float32
+	resultBytes := result.Bytes()
+	resultFloats := make([]float32, len(resultBytes)/4)
+	for i := range resultFloats {
+		bits := binary.LittleEndian.Uint32(resultBytes[i*4 : (i+1)*4])
+		resultFloats[i] = math.Float32frombits(bits)
+	}
+
+	// Compute statistics
+	var minVal, maxVal, sum float32
+	minVal = resultFloats[0]
+	maxVal = resultFloats[0]
+	for _, v := range resultFloats {
+		if v < minVal {
+			minVal = v
+		}
+		if v > maxVal {
+			maxVal = v
+		}
+		sum += v
+	}
+	mean := sum / float32(len(resultFloats))
+
+	// Build readable output
+	var sb strings.Builder
+	sb.WriteString("# MLP Forward Output\n\n")
+	sb.WriteString(fmt.Sprintf("# Input Shape: [%d, %d] (hiddenSize, batchSize)\n", hiddenSize, batchSize))
+	sb.WriteString(fmt.Sprintf("# Output Shape: %v\n", result.Shape()))
+	sb.WriteString(fmt.Sprintf("# DType: %v\n", result.DType()))
+	sb.WriteString(fmt.Sprintf("# Layer: 0\n\n"))
+
+	sb.WriteString("## Statistics\n\n")
+	sb.WriteString(fmt.Sprintf("  Total elements: %d\n", len(resultFloats)))
+	sb.WriteString(fmt.Sprintf("  Min: %v\n", minVal))
+	sb.WriteString(fmt.Sprintf("  Max: %v\n", maxVal))
+	sb.WriteString(fmt.Sprintf("  Mean: %v\n\n", mean))
+
+	sb.WriteString("## Input Hidden States (first 20 values)\n\n")
+	sb.WriteString("  [")
+	for i := 0; i < min(20, len(hsFloats)); i++ {
+		if i > 0 {
+			sb.WriteString(", ")
+		}
+		sb.WriteString(fmt.Sprintf("%v", hsFloats[i]))
+	}
+	sb.WriteString("]\n\n")
+
+	sb.WriteString("## Output Values\n\n")
+
+	// Per-position output (each position in batch)
+	for pos := 0; pos < batchSize; pos++ {
+		sb.WriteString(fmt.Sprintf("Position %d (hiddenSize=%d values):\n", pos, hiddenSize))
+
+		// Extract values for this position
+		posStart := pos * hiddenSize
+		posEnd := posStart + hiddenSize
+		if posEnd > len(resultFloats) {
+			posEnd = len(resultFloats)
+		}
+		posValues := resultFloats[posStart:posEnd]
+
+		// Full tensor values
+		sb.WriteString("  [")
+		for i, v := range posValues {
+			if i > 0 {
+				sb.WriteString(", ")
+			}
+			sb.WriteString(fmt.Sprintf("%v", v))
+		}
+		sb.WriteString("]\n\n")
+	}
+
+	// Save to file
+	if err := os.WriteFile("/tmp/olmo_mlp_forward.txt", []byte(sb.String()), 0644); err != nil {
+		t.Fatal(err)
+	}
+	t.Log("Saved /tmp/olmo_mlp_forward.txt")
+
+	// Also save binary
+	if err := os.WriteFile("/tmp/olmo_mlp_forward.bin", resultBytes, 0644); err != nil {
+		t.Fatal(err)
+	}
+	t.Log("Saved /tmp/olmo_mlp_forward.bin")
+
+	// Print summary to console
+	fmt.Println(sb.String())
+}
+
+func TestFullForward(t *testing.T) {
+	if args.model == "" {
+		t.Skip("no model specified, use -model flag")
+	}
+
+	m, err := model.New(blob(t, args.model), ml.BackendParams{AllocMemory: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if err := m.Backend().Load(t.Context(), func(float32) {}); err != nil {
+		t.Fatal(err)
+	}
+
+	ctx := m.Backend().NewContext()
+
+	prompt := args.prompt
+	if prompt == "" {
+		prompt = "Hello, how are you?"
+	}
+
+	tp := m.(model.TextProcessor)
+	tokens, err := tp.Encode(prompt, false)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	t.Logf("prompt: %q", prompt)
+	t.Logf("tokens: %v", tokens)
+
+	decoded, err := tp.Decode(tokens)
+	if err != nil {
+		t.Fatal(err)
+	}
+	t.Logf("decoded: %q", decoded)
+
+	seqLen := len(tokens)
+	inputsTensor := ctx.Input().FromInts(tokens, seqLen)
+	positions := make([]int32, seqLen)
+	sequences := make([]int, seqLen)
+	for i := range tokens {
+		positions[i] = int32(i)
+		sequences[i] = 0
+	}
+
+	// Output ALL positions
+	outputIndices := make([]int32, seqLen)
+	for i := range outputIndices {
+		outputIndices[i] = int32(i)
+	}
+	outputs := ctx.Input().FromInts(outputIndices, seqLen)
+
+	batch := input.Batch{
+		Inputs:    inputsTensor,
+		Positions: positions,
+		Sequences: sequences,
+		Outputs:   outputs,
+	}
+
+	// Initialize cache
+	if cache := m.Config().Cache; cache != nil {
+		cache.Init(m.Backend(), ml.DTypeF16, 1, 4096, seqLen)
+	}
+
+	result, err := model.Forward(ctx, m, batch)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	result = result.Contiguous(ctx)
+	ctx.Forward(result).Compute(result)
+
+	t.Logf("Forward pass completed, result shape: %v", result.Shape())
+
+	// Dump logits to binary file
+	if err := os.WriteFile("/tmp/olmo_logits.bin", result.Bytes(), 0644); err != nil {
+		t.Fatal(err)
+	}
+	t.Log("Saved /tmp/olmo_logits.bin")
+
+	// Parse logits from bytes for detailed analysis
+	logitsBytes := result.Bytes()
+	vocabSize := result.Shape()[0]
+
+	// Read float32 values - shape is (vocab_size, seq_len)
+	allLogits := make([]float32, len(logitsBytes)/4)
+	for i := range allLogits {
+		bits := binary.LittleEndian.Uint32(logitsBytes[i*4 : (i+1)*4])
+		allLogits[i] = math.Float32frombits(bits)
+	}
+
+	// Create detailed text dump matching Python format
+	var sb strings.Builder
+	sb.WriteString("# Full Forward Logits\n\n")
+	sb.WriteString(fmt.Sprintf("# Shape: [1, %d, %d]\n", seqLen, vocabSize))
+	sb.WriteString(fmt.Sprintf("# Layout: (batch=1, seq_len=%d, vocab_size=%d)\n", seqLen, vocabSize))
+	sb.WriteString(fmt.Sprintf("# Prompt: '%s'\n", prompt))
+	sb.WriteString(fmt.Sprintf("# Tokens: %v\n\n", tokens))
+
+	type logitPair struct {
+		tokenID int
+		value   float32
+	}
+
+	// Process each position
+	for pos := 0; pos < seqLen; pos++ {
+		// Extract logits for this position
+		// Shape is (vocab_size, seq_len), so logits[v*seqLen + pos] gives logit for vocab v at position pos
+		posLogits := make([]float32, vocabSize)
+		for v := 0; v < vocabSize; v++ {
+			posLogits[v] = allLogits[v*seqLen+pos]
+		}
+
+		// Find top 10 logits
+		pairs := make([]logitPair, len(posLogits))
+		for i, v := range posLogits {
+			pairs[i] = logitPair{tokenID: i, value: v}
+		}
+		// Sort by value descending (simple bubble sort for small top-k)
+		for i := 0; i < min(10, len(pairs)); i++ {
+			for j := i + 1; j < len(pairs); j++ {
+				if pairs[j].value > pairs[i].value {
+					pairs[i], pairs[j] = pairs[j], pairs[i]
+				}
+			}
+		}
+
+		tokenStr, _ := tp.Decode([]int32{tokens[pos]})
+		sb.WriteString(fmt.Sprintf("Position %d (token_id=%d, token='%s'):\n", pos, tokens[pos], tokenStr))
+		sb.WriteString("  Top 10 logits:\n")
+		for i := 0; i < min(10, len(pairs)); i++ {
+			tokStr, _ := tp.Decode([]int32{int32(pairs[i].tokenID)})
+			// Pad token string to 20 chars for alignment
+			paddedTok := fmt.Sprintf("%-20s", fmt.Sprintf("'%s'", tokStr))
+			sb.WriteString(fmt.Sprintf("    %d. token_id=%6d (%s): %f\n", i+1, pairs[i].tokenID, paddedTok, pairs[i].value))
+		}
+
+		// First 20 logits
+		sb.WriteString("  Full logits (first 20): [")
+		for i := 0; i < min(20, len(posLogits)); i++ {
+			if i > 0 {
+				sb.WriteString(", ")
+			}
+			sb.WriteString(fmt.Sprintf("%v", posLogits[i]))
+		}
+		sb.WriteString("]\n")
+
+		// Last 20 logits
+		sb.WriteString("  Full logits (last 20):  [")
+		start := max(0, len(posLogits)-20)
+		for i := start; i < len(posLogits); i++ {
+			if i > start {
+				sb.WriteString(", ")
+			}
+			sb.WriteString(fmt.Sprintf("%v", posLogits[i]))
+		}
+		sb.WriteString("]\n\n")
+	}
+
+	if err := os.WriteFile("/tmp/olmo_logits.txt", []byte(sb.String()), 0644); err != nil {
+		t.Fatal(err)
+	}
+	t.Log("Saved /tmp/olmo_logits.txt")
+
+	// Print to console as well
+	fmt.Println(sb.String())
+}
+
+func TestRoPE(t *testing.T) {
+	if args.model == "" {
+		t.Skip("no model specified, use -model flag")
+	}
+
+	m, err := model.New(blob(t, args.model), ml.BackendParams{AllocMemory: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if err := m.Backend().Load(t.Context(), func(float32) {}); err != nil {
+		t.Fatal(err)
+	}
+
+	olmoModel := m.(*Model)
+
+	// Test RoPE on a simple tensor
+	headDim := olmoModel.hiddenSize / olmoModel.numHeads
+	batchSize := 4
+	numHeads := olmoModel.numHeads
+
+	t.Logf("headDim: %d, numHeads: %d", headDim, numHeads)
+	t.Logf("ropeBase: %f, ropeScale: %f, originalContextLength: %d",
+		olmoModel.ropeBase, olmoModel.ropeScale, olmoModel.originalContextLength)
+
+	// Create test query tensor: (headDim, numHeads, batchSize)
+	queryFloats := make([]float32, headDim*numHeads*batchSize)
+	for i := range queryFloats {
+		queryFloats[i] = float32(i%100) / 100.0
+	}
+
+	// Test 1: Dump initial query values (fresh context)
+	{
+		ctx := m.Backend().NewContext()
+		query := ctx.Input().FromFloats(queryFloats, headDim, numHeads, batchSize)
+		t.Logf("query shape: %v", query.Shape())
+		query = query.Contiguous(ctx)
+		ctx.Forward(query).Compute(query)
+		dump := ml.Dump(ctx, query, ml.DumpWithPrecision(6), ml.DumpWithThreshold(1000000))
+		t.Logf("Query BEFORE RoPE sample values: %s", dump[:min(500, len(dump))])
+
+		// Write to file
+		header := fmt.Sprintf("Shape: %v\nDType: %v\n\n", query.Shape(), query.DType())
+		if err := os.WriteFile("/tmp/olmo_query_before_rope.txt", []byte(header+dump), 0644); err != nil {
+			t.Errorf("Failed to write file: %v", err)
+		}
+		if err := os.WriteFile("/tmp/olmo_query_before_rope.bin", query.Bytes(), 0644); err != nil {
+			t.Errorf("Failed to write binary file: %v", err)
+		}
+		t.Log("Wrote /tmp/olmo_query_before_rope.txt and .bin")
+	}
+
+	// Test 2: SWA RoPE (fresh context)
+	{
+		ctx := m.Backend().NewContext()
+		query := ctx.Input().FromFloats(queryFloats, headDim, numHeads, batchSize)
+		positions := ctx.Input().FromInts([]int32{0, 1, 2, 3}, batchSize)
+		resultSWA := olmoModel.applyRoPE(ctx, query, positions, headDim, true)
+		resultSWA = resultSWA.Contiguous(ctx)
+		ctx.Forward(resultSWA).Compute(resultSWA)
+		t.Logf("SWA RoPE result shape: %v", resultSWA.Shape())
+		dump := ml.Dump(ctx, resultSWA, ml.DumpWithPrecision(6), ml.DumpWithThreshold(1000000))
+		t.Logf("Query AFTER SWA RoPE sample values: %s", dump[:min(500, len(dump))])
+
+		// Write to file
+		header := fmt.Sprintf("Shape: %v\nDType: %v\nfreqScale: 1.0 (SWA)\n\n", resultSWA.Shape(), resultSWA.DType())
+		if err := os.WriteFile("/tmp/olmo_query_after_swa_rope.txt", []byte(header+dump), 0644); err != nil {
+			t.Errorf("Failed to write file: %v", err)
+		}
+		if err := os.WriteFile("/tmp/olmo_query_after_swa_rope.bin", resultSWA.Bytes(), 0644); err != nil {
+			t.Errorf("Failed to write binary file: %v", err)
+		}
+		t.Log("Wrote /tmp/olmo_query_after_swa_rope.txt and .bin")
+	}
+
+	// Test 3: Global (non-SWA) RoPE (fresh context)
+	{
+		ctx := m.Backend().NewContext()
+		query := ctx.Input().FromFloats(queryFloats, headDim, numHeads, batchSize)
+		positions := ctx.Input().FromInts([]int32{0, 1, 2, 3}, batchSize)
+		resultGlobal := olmoModel.applyRoPE(ctx, query, positions, headDim, false)
+		resultGlobal = resultGlobal.Contiguous(ctx)
+		ctx.Forward(resultGlobal).Compute(resultGlobal)
+		t.Logf("Global RoPE result shape: %v", resultGlobal.Shape())
+		dump := ml.Dump(ctx, resultGlobal, ml.DumpWithPrecision(6), ml.DumpWithThreshold(1000000))
+		t.Logf("Query AFTER Global RoPE sample values: %s", dump[:min(500, len(dump))])
+
+		// Write to file
+		header := fmt.Sprintf("Shape: %v\nDType: %v\nfreqScale: %f (Global, 1/ropeScale)\n\n",
+			resultGlobal.Shape(), resultGlobal.DType(), 1.0/olmoModel.ropeScale)
+		if err := os.WriteFile("/tmp/olmo_query_after_global_rope.txt", []byte(header+dump), 0644); err != nil {
+			t.Errorf("Failed to write file: %v", err)
+		}
+		if err := os.WriteFile("/tmp/olmo_query_after_global_rope.bin", resultGlobal.Bytes(), 0644); err != nil {
+			t.Errorf("Failed to write binary file: %v", err)
+		}
+		t.Log("Wrote /tmp/olmo_query_after_global_rope.txt and .bin")
+	}
+}

model/parsers/olmo3.go

@@ -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
+}

model/parsers/olmo3_test.go

@@ -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)
+			}
+		})
+	}
+}

model/parsers/parsers.go

@@ -60,6 +60,8 @@ func ParserForName(name string) Parser {
 		return &CogitoParser{}
 	case "olmo3-think":
 		return &Olmo3ThinkParser{}
+	case "olmo3":
+		return &Olmo3Parser{}
 	default:
 		return nil
 	}

model/renderers/olmo3.go

@@ -0,0 +1,148 @@
+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\n")
+	}
+
+	return sb.String(), nil
+}
+

model/renderers/olmo3_test.go

@@ -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)
+			}
+		})
+	}
+}

model/renderers/renderer.go

@@ -62,6 +62,9 @@ func rendererForName(name string) Renderer {
 	case "olmo3-think":
 		renderer := &Olmo3ThinkRenderer{}
 		return renderer
+	case "olmo3":
+		renderer := &Olmo3Renderer{}
+		return renderer
 	default:
 		return nil
 	}