- 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`.
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.
- 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
- /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
- 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
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.
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
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.
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.
* tokenizer: add SentencePiece-style BPE support
Add WithSentencePieceNormalizer option to BytePairEncoding for models
that use BPE with SentencePiece-style space markers (space to/from
U+2581).
NewBytePairEncoding is unchanged; the new NewBytePairEncodingWithOptions
constructor accepts BPEOption functions. Decoding handles the reverse
mapping of U+2581 back to spaces.
* review comments
Replace hardcoded Encode(prompt, true) with
Encode(prompt, r.Tokenizer.AddBOS()) so the pipeline respects each
model's tokenizer configuration.
Models with add_bos_token=true (gemma3, llama): unchanged, tokenizer
still prepends BOS.
Models with bos_token=null (qwen3, qwen3.5): unchanged, the BOS
guard (vocab.BOS >= 0) already prevented prepending regardless of
the flag.
This aligns the pipeline with the /v1/tokenize endpoint which already
uses Tokenizer.AddBOS().
pullModelManifest unmarshals the registry response into a Go struct
then re-marshals with json.Marshal before writing to disk. When the
registry's JSON formatting or field ordering differs from Go's
output, the local SHA256 won't match the registry's
Ollama-Content-Digest header, causing false "out of date" warnings.
Preserve the raw bytes from the registry response and write them
directly to disk so the local manifest is byte-for-byte identical
to what the registry serves.
* anthropic: fix empty inputs in content blocks
When we switched to `api.ToolCallFunctionArguments`, `omitempty` stopped
doing what we were relying on it for before. This would cause non-tool
content blocks to have an `"input": {}` field, which doesn't match our
old behavior.
* use omitzero instead
The staleness check compared the local manifest digest (SHA256 of the
file on disk) against the registry's Ollama-Content-Digest header.
These never matched because PullModel re-serializes the manifest JSON
before writing, producing different bytes than the registry's original.
The fallback comparison (local modified_at vs upstream push time) was
also broken: the generated TypeScript Time class discards the actual
timestamp value, so Date parsing always produced NaN.
Fix by moving the staleness comparison server-side where we have
reliable access to both the local manifest file mtime and the upstream
push time. The /api/v1/model/upstream endpoint now returns a simple
`stale` boolean instead of raw digests for the frontend to compare.
Also adds User-Agent to the CORS allowed headers for dev mode.
A stop-gap for now to guide users better. We'll add more in-depth recommendations per integration as well.
---------
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Add periodic snapshots every 8k tokens and near the end of the prompt
so that long prompts can be partially restored and thinking/generation
can be retried without full reprocessing.
Update LRU last used time just on the nodes that actually used
during processing rather than all snapshots along the path. This
allows eviction to remove nodes more accurately so we can avoid
other heuristics to auto-merge nodes.
