jmorganca 3a57b89d54 llama/compat: apply LLaMA RoPE permute to mistral3 vision Q/K
The previous mistral3 vision handler loaded but produced hallucinated
descriptions of real images (e.g. described a clear photo of a person
in a suit handing money as "abstract hexagonal pattern"). Solid colors
and large color blocks worked, suggesting the vision tower was producing
*some* signal but the spatial/feature relationships were scrambled.

Root cause: Ollama's mistral3 converter only applies its LLaMA-style
RoPE repack to TEXT-side `attn_q`/`attn_k` tensors — the
`if !HasPrefix(name, "v.")` guard in convert/convert_mistral.go::Tensors
skips vision tensors entirely. Vision Q/K therefore leave the converter
in raw HF/PyTorch order. Upstream's HF→GGUF flow does permute vision
Q/K with the vision head count, because pixtral's clip graph uses
`ggml_rope_ext` in mode 0 which expects the [n_head, head_dim/2, 2, ...]
LLaMA layout.

Fix: register a load-time op that applies LlamaModel.permute equivalently
on each F16 vision attn_q.weight / attn_k.weight (24 layers × 2 = 48
tensors for ministral-3 8B). Capture file offsets *before* renames
invalidate them, same pattern as promote_tensor_to_f32 and
register_concat_load.

Verified against the upstream-format migration mmproj (which gets the
same image right) — both 8B Ollama+compat and 14B migration now produce
matching, accurate descriptions ("hand giving money to person in suit,
cartoonish star/burst above their head, blue grid background").
2026-04-20 09:29:34 -07:00
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2026-04-02 11:33:33 -07:00
2026-04-02 11:33:33 -07:00
2023-08-22 09:40:58 -07:00
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2026-03-23 11:28:44 -07:00

ollama

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curl http://localhost:11434/api/chat -d '{
  "model": "gemma3",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'

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pip install ollama
from ollama import chat

response = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response.message.content)

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npm i ollama
import ollama from "ollama";

const response = await ollama.chat({
  model: "gemma3",
  messages: [{ role: "user", content: "Why is the sky blue?" }],
});
console.log(response.message.content);

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