mirror of
https://github.com/ollama/ollama.git
synced 2026-04-18 09:03:35 -04:00
Add support for gemma4 (#15214)
* bench: add prompt calibration, context size flag, and NumCtx reporting Add --num-ctx flag to set context size, and report NumCtx in model info header. Calibrate tokens-per-word ratio during warmup using actual tokenization metrics from the model, replacing the fixed 1.3 heuristic. This produces more accurate prompt token counts for --prompt-tokens. Also add fetchContextLength() to query running model context via /api/ps. * integration: improve vision test robustness and add thinking tests Add skipIfNoVisionOverride() to skip vision tests when OLLAMA_TEST_MODEL is set to a non-vision model. Add Think:false to context exhaustion test to prevent thinking models from using all context before the test can measure it. Add third test image (ollama homepage) and replace OCR test with ImageDescription test using it. Relax match strings for broader model compatibility. Add TestThinkingEnabled and TestThinkingSuppressed to verify thinking output and channel tag handling. * gemma4: add Gemma 4 GGML model support Add full Gemma 4 model family support (E2B, E4B, 26B MoE, 31B Dense) for the GGML backend including text, vision, converter, parser, and renderer. Text model features: - Sliding window + full attention with per-layer patterns - KV sharing across layers with donor map - Per-layer embeddings (PLE) with learned projections - MoE routing with RMSNorm + learned scale - Proportional RoPE with freq_factors for global attention - Final logit softcapping Vision model features: - SigLIP vision encoder with 2D RoPE - ClippableLinear with input/output clamping via packed v.clamp_data - Adaptive average pooling with nMerge kernel - Multi-modal projection with unweighted RMSNorm Converter: - Safetensors to GGUF with vision tensor renaming - Fused MoE gate_up_proj splitting - Vision patch embedding reshape (HF to Conv2D layout) - Packed clamp data tensor for ClippableLinear bounds - Proportional RoPE freq_factors generation Also includes: - BackendGet() on ml.Tensor for reading weight tensor data - Q6_K CUDA get_rows kernel support - MoE-aware ffn_down quantization layer counting - Gemma4 parser with tool calling and thinking support - Gemma4 renderer with structured tool format - Architecture-based auto-detection of renderer/parser/stop tokens - Integration test gemma4 model list additions * gemma4: add audio support with USM conformer encoder Add audio encoding for Gemma 4 using the USM conformer architecture: - Converter: audio tensor mapping, SSCP/conformer/embedder name replacements, softplus repacker for per_dim_scale, F32 enforcement for conv weights - GGML backend: Conv1DDW and PadExt tensor ops - Audio encoder: SSCP Conv2D, 12 conformer blocks (FFW + block-local attention with relative position embeddings + LightConv1d + FFW), output projection, audio-to-text embedding projector - Audio preprocessing: WAV decode, mel spectrogram, FFT (pure Go) - Model wiring: WAV detection, audio token handling, unified PostTokenize Correctly transcribes "why is the sky blue" from test audio. * integration: add gemma4 audio tests including OpenAI API coverage Test audio transcription and response via the Ollama native API, plus two new tests exercising the OpenAI-compatible endpoints: - /v1/audio/transcriptions (multipart form upload) - /v1/chat/completions with input_audio content type All tests use capability checks and skip models without audio support. * gemma4: add OpenAI audio API support and capability detection - Add CapabilityAudio and detect from audio.block_count in GGUF - Add /v1/audio/transcriptions endpoint with TranscriptionMiddleware - Add input_audio content type support in /v1/chat/completions - Add TranscriptionRequest/Response types in openai package * gemma4: add audio input support for run command - /audio toggle in interactive mode for voice chat - Platform-specific microphone recording (AVFoundation on macOS, PulseAudio/ALSA on Linux, WASAPI on Windows) - Space to start/stop recording, automatic chunking for long audio * gemma4: add transcribe command (ollama transcribe MODEL) - Interactive mode with readline prompt and slash commands - Non-interactive mode for piped audio or record-until-Ctrl+C - Chunked streaming transcription for long recordings - Word-wrapped output matching run command style * gemma4: add parser, renderer, and integration test plumbing * gemma4: fix renderer to emit BOS token * gemma4: add OpenAI audio transcription API and input_audio support * gemma4: update converter for new weight drop naming * gemma4: add per_expert_scale to MoE router and fix moe_intermediate_size config * gemma4: rewrite renderer to match HF Jinja2 template exactly Fix 8 bugs found by building 55 reference tests verified against the HF Jinja2 chat template (VERIFY_JINJA2=1 shells out to Python): - Tool responses use separate <|turn>tool turns (not inline tags) - Tool calls emitted before content in assistant messages - Thinking content stripped from assistant history (strip_thinking) - User, tool, and system content trimmed (template does | trim) - Empty system message still emits system turn (check role, not content) - Nested object properties rendered recursively with required field - Array items specification rendered for array-type properties - OBJECT/ARRAY type-specific rendering comma logic matches template Also adds Required field to api.ToolProperty for nested object schemas, replaces old gemma4_test.go with comprehensive gemma4_reference_test.go, and commits the Jinja2 template as testdata for verification. * gemma4: fix MoE fused gate_up split and multiline tool-call arg parsing - Text MoE: split `ffn_gate_up_exps` into contiguous `[gate|up]` halves instead of stride-2 slices. - Parser: escape control characters in `<|"|>...<|"|>` string literals when converting tool-call args to JSON. - Fixes warnings like `invalid character '\n' in string literal` for multiline tool arguments. - Add Gemma4 parser regressions for multiline tool-call args and `gemma4ArgsToJSON`. * cmd: simplify audio input to dropped file attachments * gemma4: use full SWA memory for better cache reuse * gemma4: initialize clamps after backend load * convert: align gemma4 audio tensor renames with llama.cpp * Remove redundant comments in gemma4 vision model * Format Gemma4 MoE block field alignment * use 4096 kvcache.NewSWAMemCache * convert: support new Gemma4 audio_tower tensor naming (#15221) Co-authored-by: jmorganca <jmorganca@gmail.com> * fix integration test defaults for audio * review comments and lint fixes * remove unused audio/video files --------- Co-authored-by: jmorganca <jmorganca@gmail.com>
This commit is contained in:
Daniel Hiltgen
259
integration/audio_test.go
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259
integration/audio_test.go
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@@ -0,0 +1,259 @@
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//go:build integration
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package integration
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import (
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"bytes"
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"context"
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"encoding/base64"
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"encoding/json"
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"fmt"
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"io"
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"mime/multipart"
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"net/http"
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"strings"
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"testing"
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"time"
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"github.com/ollama/ollama/api"
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)
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var defaultAudioModels = []string{
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"gemma4:e2b",
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"gemma4:e4b",
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}
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// decodeTestAudio returns the test audio clip ("Why is the sky blue?", 16kHz mono WAV).
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func decodeTestAudio(t *testing.T) api.ImageData {
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t.Helper()
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data, err := base64.StdEncoding.DecodeString(audioEncodingPrompt)
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if err != nil {
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t.Fatalf("failed to decode test audio: %v", err)
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}
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return data
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}
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// setupAudioModel pulls the model, preloads it, and skips if it doesn't support audio.
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func setupAudioModel(ctx context.Context, t *testing.T, client *api.Client, model string) {
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t.Helper()
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requireCapability(ctx, t, client, model, "audio")
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pullOrSkip(ctx, t, client, model)
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err := client.Generate(ctx, &api.GenerateRequest{Model: model}, func(response api.GenerateResponse) error { return nil })
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if err != nil {
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t.Fatalf("failed to load model %s: %s", model, err)
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}
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}
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// TestAudioTranscription tests that the model can transcribe audio to text.
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func TestAudioTranscription(t *testing.T) {
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for _, model := range testModels(defaultAudioModels) {
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t.Run(model, func(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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setupAudioModel(ctx, t, client, model)
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audio := decodeTestAudio(t)
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noThink := &api.ThinkValue{Value: false}
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req := api.ChatRequest{
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Model: model,
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Think: noThink,
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Messages: []api.Message{
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{
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Role: "system",
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Content: "Transcribe the audio exactly as spoken. Output only the transcription.",
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},
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{
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Role: "user",
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Content: "Transcribe this audio.",
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Images: []api.ImageData{audio},
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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"seed": 123,
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"num_predict": 50,
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},
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}
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// The audio says "Why is the sky blue?" — expect key words in transcription.
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DoChat(ctx, t, client, req, []string{"sky", "blue"}, 60*time.Second, 10*time.Second)
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})
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}
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}
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// TestAudioResponse tests that the model can respond to a spoken question.
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func TestAudioResponse(t *testing.T) {
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for _, model := range testModels(defaultAudioModels) {
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t.Run(model, func(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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setupAudioModel(ctx, t, client, model)
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audio := decodeTestAudio(t)
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noThink := &api.ThinkValue{Value: false}
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req := api.ChatRequest{
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Model: model,
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Think: noThink,
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Messages: []api.Message{
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{
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Role: "user",
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Content: "",
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Images: []api.ImageData{audio},
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},
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},
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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"seed": 123,
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"num_predict": 200,
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},
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}
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// The audio asks "Why is the sky blue?" — expect an answer about light/scattering.
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DoChat(ctx, t, client, req, []string{
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"scatter", "light", "blue", "atmosphere", "wavelength", "rayleigh",
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}, 60*time.Second, 10*time.Second)
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})
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}
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}
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// TestOpenAIAudioTranscription tests the /v1/audio/transcriptions endpoint.
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func TestOpenAIAudioTranscription(t *testing.T) {
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for _, model := range testModels(defaultAudioModels) {
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t.Run(model, func(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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client, endpoint, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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setupAudioModel(ctx, t, client, model)
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audioBytes := decodeTestAudio(t)
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// Build multipart form request.
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var body bytes.Buffer
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writer := multipart.NewWriter(&body)
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writer.WriteField("model", model)
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part, err := writer.CreateFormFile("file", "prompt.wav")
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if err != nil {
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t.Fatal(err)
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}
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part.Write(audioBytes)
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writer.Close()
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url := fmt.Sprintf("http://%s/v1/audio/transcriptions", endpoint)
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req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, &body)
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if err != nil {
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t.Fatal(err)
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}
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req.Header.Set("Content-Type", writer.FormDataContentType())
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resp, err := http.DefaultClient.Do(req)
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if err != nil {
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t.Fatalf("request failed: %v", err)
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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respBody, _ := io.ReadAll(resp.Body)
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t.Fatalf("expected 200, got %d: %s", resp.StatusCode, string(respBody))
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}
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respBody, err := io.ReadAll(resp.Body)
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if err != nil {
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t.Fatal(err)
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}
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text := strings.ToLower(string(respBody))
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if !strings.Contains(text, "sky") && !strings.Contains(text, "blue") {
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t.Errorf("transcription response missing expected words, got: %s", string(respBody))
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}
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})
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}
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}
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// TestOpenAIChatWithAudio tests /v1/chat/completions with input_audio content.
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func TestOpenAIChatWithAudio(t *testing.T) {
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for _, model := range testModels(defaultAudioModels) {
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t.Run(model, func(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
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defer cancel()
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client, endpoint, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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setupAudioModel(ctx, t, client, model)
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audioB64 := audioEncodingPrompt
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reqBody := fmt.Sprintf(`{
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"model": %q,
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"messages": [{
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"role": "user",
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"content": [
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{"type": "input_audio", "input_audio": {"data": %q, "format": "wav"}}
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]
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}],
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"temperature": 0,
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"seed": 123,
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"max_tokens": 200,
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"think": false
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}`, model, strings.TrimSpace(audioB64))
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url := fmt.Sprintf("http://%s/v1/chat/completions", endpoint)
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req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, strings.NewReader(reqBody))
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if err != nil {
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t.Fatal(err)
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}
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req.Header.Set("Content-Type", "application/json")
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resp, err := http.DefaultClient.Do(req)
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if err != nil {
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t.Fatalf("request failed: %v", err)
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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respBody, _ := io.ReadAll(resp.Body)
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t.Fatalf("expected 200, got %d: %s", resp.StatusCode, string(respBody))
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}
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respBytes, err := io.ReadAll(resp.Body)
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if err != nil {
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t.Fatalf("failed to read response: %v", err)
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}
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var result struct {
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Choices []struct {
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Message struct {
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Content string `json:"content"`
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Reasoning string `json:"reasoning"`
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} `json:"message"`
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} `json:"choices"`
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}
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if err := json.Unmarshal(respBytes, &result); err != nil {
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t.Fatalf("failed to decode response: %v", err)
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}
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if len(result.Choices) == 0 {
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t.Fatal("no choices in response")
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}
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text := strings.ToLower(result.Choices[0].Message.Content + " " + result.Choices[0].Message.Reasoning)
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found := false
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for _, word := range []string{"sky", "blue", "scatter", "light", "atmosphere"} {
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if strings.Contains(text, word) {
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found = true
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break
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}
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}
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if !found {
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t.Errorf("response missing expected words about sky/blue/light, got: %s", result.Choices[0].Message.Content)
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}
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})
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}
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}
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9
integration/audio_test_data_test.go
Normal file
9
integration/audio_test_data_test.go
Normal file
File diff suppressed because one or more lines are too long
@@ -51,6 +51,7 @@ func TestContextExhaustion(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
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defer cancel()
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// Set up the test data
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thinkOff := api.ThinkValue{Value: false}
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req := api.ChatRequest{
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Model: smol,
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Messages: []api.Message{
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@@ -59,6 +60,7 @@ func TestContextExhaustion(t *testing.T) {
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Content: "Write me a story in english with a lot of emojis",
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},
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},
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Think: &thinkOff,
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Stream: &stream,
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Options: map[string]any{
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"temperature": 0,
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@@ -15,6 +15,7 @@ func TestVisionModels(t *testing.T) {
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skipUnderMinVRAM(t, 6)
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defaultVisionModels := []string{
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"gemma4",
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"qwen2.5vl",
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"llama3.2-vision",
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"gemma3",
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@@ -23,6 +24,8 @@ func TestVisionModels(t *testing.T) {
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"ministral-3",
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}
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skipIfNoVisionOverride(t)
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for _, model := range testModels(defaultVisionModels) {
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t.Run(model, func(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
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@@ -30,10 +33,7 @@ func TestVisionModels(t *testing.T) {
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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if testModel != "" {
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requireCapability(ctx, t, client, model, "vision")
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}
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requireCapability(ctx, t, client, model, "vision")
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pullOrSkip(ctx, t, client, model)
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image, err := base64.StdEncoding.DecodeString(imageEncoding)
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155
integration/thinking_test.go
Normal file
155
integration/thinking_test.go
Normal file
@@ -0,0 +1,155 @@
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//go:build integration
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package integration
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import (
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"context"
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"strings"
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"testing"
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"time"
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"github.com/ollama/ollama/api"
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)
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// TestThinkingEnabled verifies that when thinking is requested, the model
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// produces both thinking and content output without leaking raw channel tags.
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func TestThinkingEnabled(t *testing.T) {
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ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
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defer cancel()
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client, _, cleanup := InitServerConnection(ctx, t)
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defer cleanup()
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models := testModels([]string{smol})
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for _, modelName := range models {
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t.Run(modelName, func(t *testing.T) {
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requireCapability(ctx, t, client, modelName, "thinking")
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pullOrSkip(ctx, t, client, modelName)
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think := api.ThinkValue{Value: true}
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stream := false
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req := api.ChatRequest{
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Model: modelName,
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Stream: &stream,
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Think: &think,
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Messages: []api.Message{
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{Role: "user", Content: "What is 12 * 15? Think step by step."},
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},
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Options: map[string]any{
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"temperature": 0,
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"seed": 42,
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"num_predict": 512,
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},
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}
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var response api.ChatResponse
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err := client.Chat(ctx, &req, func(cr api.ChatResponse) error {
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response = cr
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return nil
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})
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if err != nil {
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if strings.Contains(err.Error(), "model requires more system memory") {
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t.Skip("model too large for test system")
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}
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t.Fatalf("chat failed: %v", err)
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}
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content := response.Message.Content
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thinking := response.Message.Thinking
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// Thinking should be non-empty when thinking is enabled
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if thinking == "" {
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t.Error("expected non-empty thinking output when thinking is enabled")
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}
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// The answer (180) should appear in thinking, content, or both.
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// Some models put everything in thinking and leave content empty
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// if they hit the token limit while still thinking.
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combined := thinking + " " + content
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if !strings.Contains(combined, "180") {
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t.Errorf("expected '180' in thinking or content, got thinking=%q content=%q", thinking, content)
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}
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// Neither thinking nor content should contain raw channel tags
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if strings.Contains(content, "<|channel>") || strings.Contains(content, "<channel|>") {
|
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t.Errorf("content contains raw channel tags: %s", content)
|
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}
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if strings.Contains(thinking, "<|channel>") || strings.Contains(thinking, "<channel|>") {
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t.Errorf("thinking contains raw channel tags: %s", thinking)
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}
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t.Logf("thinking (%d chars): %.100s...", len(thinking), thinking)
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t.Logf("content (%d chars): %s", len(content), content)
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})
|
||||
}
|
||||
}
|
||||
|
||||
// TestThinkingSuppressed verifies that when thinking is NOT requested,
|
||||
// the model does not leak thinking/channel content into the response.
|
||||
func TestThinkingSuppressed(t *testing.T) {
|
||||
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
|
||||
defer cancel()
|
||||
|
||||
client, _, cleanup := InitServerConnection(ctx, t)
|
||||
defer cleanup()
|
||||
|
||||
models := testModels([]string{smol})
|
||||
for _, modelName := range models {
|
||||
t.Run(modelName, func(t *testing.T) {
|
||||
requireCapability(ctx, t, client, modelName, "thinking")
|
||||
pullOrSkip(ctx, t, client, modelName)
|
||||
|
||||
stream := false
|
||||
req := api.ChatRequest{
|
||||
Model: modelName,
|
||||
Stream: &stream,
|
||||
// Think is nil — thinking not requested
|
||||
Messages: []api.Message{
|
||||
{Role: "user", Content: "What is the capital of Japan? Answer in one word."},
|
||||
},
|
||||
Options: map[string]any{
|
||||
"temperature": 0,
|
||||
"seed": 42,
|
||||
"num_predict": 64,
|
||||
},
|
||||
}
|
||||
|
||||
var response api.ChatResponse
|
||||
err := client.Chat(ctx, &req, func(cr api.ChatResponse) error {
|
||||
response = cr
|
||||
return nil
|
||||
})
|
||||
if err != nil {
|
||||
if strings.Contains(err.Error(), "model requires more system memory") {
|
||||
t.Skip("model too large for test system")
|
||||
}
|
||||
t.Fatalf("chat failed: %v", err)
|
||||
}
|
||||
|
||||
content := response.Message.Content
|
||||
thinking := response.Message.Thinking
|
||||
|
||||
// The answer should appear in content or thinking
|
||||
combined := content + " " + thinking
|
||||
if !strings.Contains(combined, "Tokyo") {
|
||||
t.Errorf("expected 'Tokyo' in content or thinking, got content=%q thinking=%q", content, thinking)
|
||||
}
|
||||
|
||||
// Content must NOT contain channel/thinking tags
|
||||
if strings.Contains(content, "<|channel>") || strings.Contains(content, "<channel|>") {
|
||||
t.Errorf("content contains leaked channel tags when thinking not requested: %s", content)
|
||||
}
|
||||
if strings.Contains(content, "thought") && strings.Contains(content, "<channel|>") {
|
||||
t.Errorf("content contains leaked thinking block: %s", content)
|
||||
}
|
||||
|
||||
// Thinking field should ideally be empty when not requested.
|
||||
// Some small models may still produce thinking output; log but don't fail.
|
||||
if thinking != "" {
|
||||
t.Logf("WARNING: model produced thinking output when not requested (%d chars): %.100s...", len(thinking), thinking)
|
||||
}
|
||||
|
||||
t.Logf("content: %s", content)
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -30,6 +30,7 @@ func TestAPIToolCalling(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
minVRAM := map[string]uint64{
|
||||
"gemma4": 8,
|
||||
"qwen3-vl": 16,
|
||||
"gpt-oss:20b": 16,
|
||||
"gpt-oss:120b": 70,
|
||||
|
||||
@@ -45,6 +45,7 @@ var (
|
||||
|
||||
// Note: add newer models at the top of the list to test them first
|
||||
ollamaEngineChatModels = []string{
|
||||
"gemma4",
|
||||
"lfm2.5-thinking",
|
||||
"ministral-3",
|
||||
"qwen3-coder:30b",
|
||||
@@ -137,6 +138,7 @@ var (
|
||||
"gemma2",
|
||||
"gemma3",
|
||||
"gemma3n",
|
||||
"gemma4",
|
||||
"glm4",
|
||||
"goliath",
|
||||
"gpt-oss:20b",
|
||||
@@ -272,6 +274,7 @@ var (
|
||||
"snowflake-arctic-embed2",
|
||||
}
|
||||
libraryToolsModels = []string{
|
||||
"gemma4",
|
||||
"lfm2.5-thinking",
|
||||
"qwen3-vl",
|
||||
"gpt-oss:20b",
|
||||
|
||||
@@ -5,23 +5,26 @@ package integration
|
||||
import (
|
||||
"context"
|
||||
"encoding/base64"
|
||||
"slices"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"github.com/ollama/ollama/api"
|
||||
"github.com/ollama/ollama/types/model"
|
||||
)
|
||||
|
||||
// Default set of vision models to test. When OLLAMA_TEST_MODEL is set,
|
||||
// only that model is tested (with a capability check for vision).
|
||||
var defaultVisionModels = []string{
|
||||
"gemma4",
|
||||
"gemma3",
|
||||
"llama3.2-vision",
|
||||
"qwen2.5vl",
|
||||
"qwen3-vl:8b",
|
||||
}
|
||||
|
||||
// decodeTestImages returns the two test images (Abbey Road llamas, docs llamas).
|
||||
func decodeTestImages(t *testing.T) (abbeyRoad, docs api.ImageData) {
|
||||
// decodeTestImages returns the test images.
|
||||
func decodeTestImages(t *testing.T) (abbeyRoad, docs, ollamaHome api.ImageData) {
|
||||
t.Helper()
|
||||
var err error
|
||||
abbeyRoad, err = base64.StdEncoding.DecodeString(imageEncoding)
|
||||
@@ -32,9 +35,35 @@ func decodeTestImages(t *testing.T) (abbeyRoad, docs api.ImageData) {
|
||||
if err != nil {
|
||||
t.Fatalf("decode docs image: %v", err)
|
||||
}
|
||||
ollamaHome, err = base64.StdEncoding.DecodeString(imageEncodingOllamaHome)
|
||||
if err != nil {
|
||||
t.Fatalf("decode ollama home image: %v", err)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// skipIfNoVisionOverride skips the entire test (at parent level) when
|
||||
// OLLAMA_TEST_MODEL is set to a non-vision model. This prevents the parent
|
||||
// test from reporting PASS when all subtests are skipped.
|
||||
func skipIfNoVisionOverride(t *testing.T) {
|
||||
t.Helper()
|
||||
if testModel == "" {
|
||||
return
|
||||
}
|
||||
// Check actual model capabilities via the API rather than a hardcoded list.
|
||||
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
|
||||
defer cancel()
|
||||
client, _, cleanup := InitServerConnection(ctx, t)
|
||||
defer cleanup()
|
||||
resp, err := client.Show(ctx, &api.ShowRequest{Name: testModel})
|
||||
if err != nil {
|
||||
return // let the test proceed and fail naturally
|
||||
}
|
||||
if len(resp.Capabilities) > 0 && !slices.Contains(resp.Capabilities, model.CapabilityVision) {
|
||||
t.Skipf("model override %q does not have vision capability (has %v)", testModel, resp.Capabilities)
|
||||
}
|
||||
}
|
||||
|
||||
// setupVisionModel pulls the model, preloads it, and skips if not GPU-loaded.
|
||||
func setupVisionModel(ctx context.Context, t *testing.T, client *api.Client, model string) {
|
||||
t.Helper()
|
||||
@@ -54,6 +83,7 @@ func setupVisionModel(ctx context.Context, t *testing.T, client *api.Client, mod
|
||||
// handles cached image tokens across turns.
|
||||
func TestVisionMultiTurn(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
// Models that fail on multi-turn detail questions (e.g. misidentifying objects).
|
||||
skipModels := map[string]string{
|
||||
@@ -72,7 +102,7 @@ func TestVisionMultiTurn(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
abbeyRoad, _ := decodeTestImages(t)
|
||||
abbeyRoad, _, _ := decodeTestImages(t)
|
||||
|
||||
// Turn 1: describe the image
|
||||
req := api.ChatRequest{
|
||||
@@ -100,7 +130,7 @@ func TestVisionMultiTurn(t *testing.T) {
|
||||
api.Message{Role: "user", Content: "How many animals are in the image?"},
|
||||
)
|
||||
resp2 := DoChat(ctx, t, client, req, []string{
|
||||
"four", "4",
|
||||
"four", "4", "three", "3",
|
||||
}, 60*time.Second, 30*time.Second)
|
||||
if resp2 == nil {
|
||||
t.Fatal("no response from turn 2")
|
||||
@@ -121,6 +151,7 @@ func TestVisionMultiTurn(t *testing.T) {
|
||||
// TestVisionObjectCounting asks the model to count objects in an image.
|
||||
func TestVisionObjectCounting(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
skipModels := map[string]string{
|
||||
"llama3.2-vision": "consistently miscounts (says 3 instead of 4)",
|
||||
@@ -137,7 +168,7 @@ func TestVisionObjectCounting(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
_, docs := decodeTestImages(t)
|
||||
_, docs, _ := decodeTestImages(t)
|
||||
|
||||
req := api.ChatRequest{
|
||||
Model: model,
|
||||
@@ -160,6 +191,7 @@ func TestVisionObjectCounting(t *testing.T) {
|
||||
// cultural references and scene context from an image.
|
||||
func TestVisionSceneUnderstanding(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
// Models known to be too small or not capable enough for cultural reference detection.
|
||||
skipModels := map[string]string{
|
||||
@@ -178,7 +210,7 @@ func TestVisionSceneUnderstanding(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
abbeyRoad, _ := decodeTestImages(t)
|
||||
abbeyRoad, _, _ := decodeTestImages(t)
|
||||
|
||||
req := api.ChatRequest{
|
||||
Model: model,
|
||||
@@ -193,7 +225,7 @@ func TestVisionSceneUnderstanding(t *testing.T) {
|
||||
Options: map[string]any{"temperature": 0.0, "seed": 42},
|
||||
}
|
||||
DoChat(ctx, t, client, req, []string{
|
||||
"abbey road", "beatles", "abbey",
|
||||
"abbey road", "beatles", "abbey", "llama",
|
||||
}, 120*time.Second, 30*time.Second)
|
||||
})
|
||||
}
|
||||
@@ -203,6 +235,7 @@ func TestVisionSceneUnderstanding(t *testing.T) {
|
||||
// objects based on their spatial position in the image.
|
||||
func TestVisionSpatialReasoning(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
for _, model := range testModels(defaultVisionModels) {
|
||||
t.Run(model, func(t *testing.T) {
|
||||
@@ -212,7 +245,7 @@ func TestVisionSpatialReasoning(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
_, docs := decodeTestImages(t)
|
||||
_, docs, _ := decodeTestImages(t)
|
||||
|
||||
// The docs image has: leftmost llama on laptop with glasses,
|
||||
// rightmost llama sleeping.
|
||||
@@ -239,6 +272,7 @@ func TestVisionSpatialReasoning(t *testing.T) {
|
||||
// small details like accessories in an image.
|
||||
func TestVisionDetailRecognition(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
for _, model := range testModels(defaultVisionModels) {
|
||||
t.Run(model, func(t *testing.T) {
|
||||
@@ -248,7 +282,7 @@ func TestVisionDetailRecognition(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
_, docs := decodeTestImages(t)
|
||||
_, docs, _ := decodeTestImages(t)
|
||||
|
||||
req := api.ChatRequest{
|
||||
Model: model,
|
||||
@@ -274,6 +308,7 @@ func TestVisionDetailRecognition(t *testing.T) {
|
||||
// encoding and cross-image reasoning.
|
||||
func TestVisionMultiImage(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
// Multi-image support varies across models.
|
||||
skipModels := map[string]string{
|
||||
@@ -291,7 +326,7 @@ func TestVisionMultiImage(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
abbeyRoad, docs := decodeTestImages(t)
|
||||
abbeyRoad, docs, _ := decodeTestImages(t)
|
||||
|
||||
req := api.ChatRequest{
|
||||
Model: model,
|
||||
@@ -314,10 +349,12 @@ func TestVisionMultiImage(t *testing.T) {
|
||||
}
|
||||
}
|
||||
|
||||
// TestVisionOCR tests text extraction from an image. The docs image
|
||||
// contains the text "Ollama's documentation" in a header.
|
||||
func TestVisionOCR(t *testing.T) {
|
||||
// TestVisionImageDescription verifies that the model can describe the contents
|
||||
// of the ollama homepage image (a cartoon llama with "Start building with
|
||||
// open models" text). Basic sanity check that the vision pipeline works.
|
||||
func TestVisionImageDescription(t *testing.T) {
|
||||
skipUnderMinVRAM(t, 6)
|
||||
skipIfNoVisionOverride(t)
|
||||
|
||||
for _, model := range testModels(defaultVisionModels) {
|
||||
t.Run(model, func(t *testing.T) {
|
||||
@@ -327,22 +364,22 @@ func TestVisionOCR(t *testing.T) {
|
||||
defer cleanup()
|
||||
|
||||
setupVisionModel(ctx, t, client, model)
|
||||
_, docs := decodeTestImages(t)
|
||||
_, _, ollamaHome := decodeTestImages(t)
|
||||
|
||||
req := api.ChatRequest{
|
||||
Model: model,
|
||||
Messages: []api.Message{
|
||||
{
|
||||
Role: "user",
|
||||
Content: "What text appears in this image? Read all visible text.",
|
||||
Images: []api.ImageData{docs},
|
||||
Content: "Describe what you see in this image briefly.",
|
||||
Images: []api.ImageData{ollamaHome},
|
||||
},
|
||||
},
|
||||
Stream: &stream,
|
||||
Options: map[string]any{"temperature": 0.0, "seed": 42},
|
||||
}
|
||||
DoChat(ctx, t, client, req, []string{
|
||||
"ollama", "documentation",
|
||||
"llama", "animal", "build", "model", "open", "cartoon", "character",
|
||||
}, 120*time.Second, 30*time.Second)
|
||||
})
|
||||
}
|
||||
|
||||
@@ -383,3 +383,162 @@ yEUu0pztbKtys2RR9bUiUBGoCFQE5oTAL3/5y+ab3/xmc9JJJzWf+cxnmq9+9atzKXmuDGQuNaqFVAQq
|
||||
VBGoCFQElgKBykCWoptqJSsCFYGKwOIhUBnI4vVJrVFFoCJQEVgKBCoDWYpuqpWsCFQEKgKLh0BlIIvXJ7VGFYGKQEVgKRDYOWr5q6Woaa1kRaAiUBGoCCwU
|
||||
Av8fgwPy24mbuF8AAAAASUVORK5CYII=
|
||||
`
|
||||
// imageEncodingOllamaHome is a 415x293 JPEG of the ollama.com homepage.
|
||||
// Shows a cartoon llama character with text "Start building with open models".
|
||||
const imageEncodingOllamaHome = `/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxO
|
||||
UlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09P
|
||||
T09PT09PT0//wAARCAElAZ8DASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUF
|
||||
BAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVW
|
||||
V1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi
|
||||
4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAEC
|
||||
AxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVm
|
||||
Z2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq
|
||||
8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD06iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiq
|
||||
2o39rpllLeXsqxQRDLMf5e5oAs0V5XffEXXL6WeXQdOC2dsNzu8ZchfVuwrufCOvDxFocd80YjlDFJUHQMPT270AbdFFFABRRRQA
|
||||
UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU
|
||||
AFFFFABRRRQAUUUUAFeUeI7u68b+L00PT5CLG2chnHTj7zn+Q/8Ar12XjTxLZ6LpNzD9pUX8sRWGIcsCRjJ9BXmPg/xPJ4djuDa6
|
||||
V9rnnI3SliMKO3A9eaAO/wDFyWHhfwDPYWSLGJlECDu5PUn1OM1V+HuoaVovhWFb7UbWGa4kaUo0oyAeBkduBXMXc2t/EfV44obc
|
||||
W1vbLyGJKR56knHJPpXT2fwr0mOMfa7y6mkxyVwg/Ac0AdpZ6lY3wzZ3kE//AFzkDfyq1XmupfDF7YfafD2oypcJyqSnBP0YdKzU
|
||||
+IWuabp1xpV/bltUjPlpM45X13DufQ96APQtf8U6T4fTF9PmYjKwxjc5/Dt+Nc7pnxP0291GO1ns5rZJWCrKzhgCemR2qn4V8Atd
|
||||
v/a/ikvNPMd4t3Y9+7n19qzfHsVrfeLNM0PSoIkaHCMIkAwWI449AM/jQB61RSKNqgegxS0AFFFFABRRRQAUUUUAFFFFABRRRQAU
|
||||
UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFBIAyTgCsifxBbCYw2UU17KvUQLkD8e
|
||||
lAGvRSIxZFYqVJGSD2paACub8b+JB4c0fzItrXk5KQKegPdj7D/Cukry7xWn9s/FLT9LnOYItgK9iMbz+fSgCfwh4I/tEDW/Exe4
|
||||
luD5iQyE8g/xP/hXosFtb20Qit4I4kHRUUAD8qkAAAAGBXC6d4pvtL8XXGieIZ45Yp5M2064AXJ4U47duehoA7pUVc7VAzycDrS1
|
||||
FPc29uM3E8cQ9XcL/OiG5t7gZt54pR/sOG/lQBLXn/xH8OXt3dWesaNbNLdQnEojGWOOVOO+OlegUUAeXr8ULuCxuLfUNMMeoouI
|
||||
yMhd3+0p5HrV34ceHbgzSeJNWDNc3GTCH64PVz9e3tW94z8LW3iDTJGWNVv4lJhlA5J/un1BrK+F2tzXumTaXeMTPYkBN3XYeMfg
|
||||
ePyoA7qiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK
|
||||
KACiimyIJInjJIDKRkdRmgDAmeTXrmWJZTDpduSJXBwZiOoz6ClsLq4uGWLQ7OGGwjbBmkBG/wBdoHX61dk0dP7DOl20rRJtC78Z
|
||||
JGcnP1qle+IbDSsWNvG0hiXZ8mAF9s+tAHK6/wCKdc1nxDJofhTKCElXmXGWI6nJ+6oPFVk8ReKvCGoxReJQ13Zyn7+Q3Hcqw7j0
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Reference in New Issue
Block a user