Initial release: Mistral Small 3.2 24B OpenThoughts Distill v1 (MLX 4bit)
Browse files- .gitattributes +1 -0
- README.md +204 -0
- chat_template.jinja +1 -0
- config.json +40 -0
- generation_config.json +7 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +935 -0
- tokenizer.json +3 -0
- tokenizer_config.json +14 -0
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README.md
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| 1 |
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---
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license: apache-2.0
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base_model:
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- mistralai/Mistral-Small-3.2-24B-Instruct-2506
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- mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit
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datasets:
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- open-thoughts/OpenThoughts-114k
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- ja
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- ko
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library_name: mlx
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pipeline_tag: text-generation
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tags:
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- mistral
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- mlx
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- apple-silicon
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- lora
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- reasoning
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- chain-of-thought
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- distillation
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- 4bit
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---
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# Mistral-Small-3.2-24B-OpenThoughts-Distill-v1 (MLX 4bit)
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A LoRA fine-tune of **Mistral Small 3.2 24B Instruct** on the **OpenThoughts-114k** reasoning dataset, intended to strengthen structured chain-of-thought reasoning patterns. Released in the **MLX 4-bit format** for Apple Silicon.
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GGUF quantizations will be released as a separate companion repository — see the Roadmap below.
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## Model Details
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| | |
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|---|---|
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| **Base model** | [`mistralai/Mistral-Small-3.2-24B-Instruct-2506`](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506), via the [`mlx-community/...-4bit`](https://huggingface.co/mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit) MLX quantization |
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| **Parameters** | 23.57 B total · 4.62 M trainable LoRA (0.020%) |
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| **Format** | MLX, 4-bit quantized |
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| **Architecture** | `Mistral3ForConditionalGeneration` (vision-capable base; this fine-tune targets the text backbone only) |
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| **Languages** | multilingual (inherited from base: en, fr, de, es, it, pt, ja, ko, …) |
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| **License** | Apache 2.0 |
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## Training
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LoRA fine-tune with [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms) on an Apple **M4 Pro** (64 GB unified memory).
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| Hyperparameter | Value |
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|---|---|
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| Method | LoRA |
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| Trainable layers | Last 4 |
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| Iterations | 1500 |
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| Batch size | 1 |
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| Gradient accumulation steps | 2 (effective batch size 2) |
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| Learning rate | 1e-5 |
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| Max sequence length | 2048 tokens |
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| Gradient checkpointing | Yes |
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| Mask prompt | Yes (only assistant turns contribute to the loss) |
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| Peak memory | ~17.7 GB |
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| Hardware | Apple M4 Pro, 64 GB unified memory |
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### Reproducible training command
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```bash
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mlx_lm.lora \
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--model mlx-community/Mistral-Small-3.2-24B-Instruct-2506-4bit \
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--train --data ./data --iters 1500 \
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--batch-size 1 --grad-accumulation-steps 2 \
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--num-layers 4 --grad-checkpoint --mask-prompt \
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--learning-rate 1e-5 --max-seq-length 2048 \
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--save-every 250 --steps-per-eval 250 \
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--adapter-path ./adapters
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```
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After training, the resulting LoRA adapter was merged into the base via `mlx_lm.fuse`.
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## Dataset
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**[`open-thoughts/OpenThoughts-114k`](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k)** — 113,957 reasoning trace examples, generated by **DeepSeek-R1**, released under **Apache 2.0**.
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The ShareGPT-style schema (`system` + `conversations[from/value]`) was converted to mlx-lm's chat-template-compatible `{"messages": […]}` JSONL format:
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| Split | Examples |
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|---|---|
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| Train | 113,457 |
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| Validation | 500 |
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### Caveat — Sequence Truncation
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OpenThoughts contains very long reasoning chains (single examples up to ~23k tokens). To fit the 64 GB Mac memory budget, `max_seq_length` was set to **2048**. As a result, the model is trained on the **first 2048 tokens** of every example and the long tails of longer reasoning chains were dropped.
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Practical implication: the model has been exposed to reasoning structures **up to 2048 tokens**. When generating responses substantially longer than that on its own, quality may degrade earlier than the chosen `max_tokens` cap. A v2 with a higher sequence length is on the roadmap.
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## Usage (MLX)
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```python
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from mlx_lm import load, generate
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model, tokenizer = load(
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"pnoid/Mistral-Small-3.2-24B-OpenThoughts-Distill-v1-mlx-4bit"
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)
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messages = [
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{"role": "user",
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"content": "Three friends share 24 cookies equally. "
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"One friend gives 3 cookies to each of the others. "
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"How many does each friend have now? "
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"Reason step by step."}
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]
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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response = generate(
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model, tokenizer, prompt=prompt, max_tokens=1024, verbose=True
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)
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```
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CLI:
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```bash
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mlx_lm.generate \
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--model pnoid/Mistral-Small-3.2-24B-OpenThoughts-Distill-v1-mlx-4bit \
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--prompt "Your reasoning question here" \
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--max-tokens 1024
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```
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## Reasoning Style — Observed Differences
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This v1 release is honestly framed as a **stylistic distillation**, not a capability leap. The goal was to transfer R1-style explicit-reasoning patterns onto a strong base. Side-by-side probes on classic reasoning prompts (cookie-share math, bat-and-ball Cognitive Reflection Test, sister-brother counting trap) gave the following picture:
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### Pattern transfer (intended outcome — present)
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The fine-tuned model exhibits R1-signature self-verification language that the base model does not, e.g.:
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> *"Let me rephrase the information to make sure I get it right…"*
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> *"Wait, let me reconsider…"*
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> *"Actually, let me check…"*
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This is the visible signature of the OpenThoughts-114k (R1-generated) reasoning data.
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### Capability gain (honest note — not demonstrated)
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On the probes above, the **base Mistral Small 3.2 24B Instruct** already produces correct, structured, didactic answers — including a spontaneous *"Common Pitfall"* warning on the bat-and-ball trap. This v1 fine-tune did **not** measurably improve accuracy on those particular probes.
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Likely contributing factors:
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- The base is already strong at structured reasoning out-of-the-box.
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- Training was on a 4-bit quantized base (gradients against quantized weights).
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- The 2048-token training truncation means the model has been exposed to *starts* of long reasoning chains but not to their *convergence*. On long traps (sister-brother), the fine-tune showed *more* self-questioning loops but did not converge faster than the base.
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### What this release is useful for
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- An MLX-format, Apache-2.0, openly-licensed-chain model that **visibly exhibits R1-style explicit reasoning** on Apple Silicon.
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- A baseline for our own iteration: a v2 trained on a higher-precision base, longer sequence length, and more iterations is on the roadmap and is where a measurable capability shift is expected.
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## License Chain
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| Component | License |
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|---|---|
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| Base model (Mistral Small 3.2 24B Instruct) | Apache 2.0 |
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| 4-bit MLX quantization (mlx-community) | Apache 2.0 |
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| Training dataset (OpenThoughts-114k) | Apache 2.0 |
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| **This LoRA adapter + merged model** | **Apache 2.0** |
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The entire supply chain is Apache 2.0. **No proprietary LLM outputs** (e.g. OpenAI, Anthropic) were used either in dataset generation or in training.
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## Roadmap
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- GGUF quantizations (at minimum Q4_K_M and Q8_0) released as a sibling `…-GGUF` repository
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- v2 with higher sequence length and more iterations once a fp16/bf16-capable host is available
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- Public evaluation on standard reasoning benchmarks (GSM8K, MATH, MMLU-Pro)
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## Limitations
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- **Sequence truncation during training** at 2048 tokens may cause earlier-than-ideal stopping behaviour on very long self-generated reasoning chains.
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- **4-bit quantized base** before LoRA: gradients are computed against the quantized weights. Quality is solid for distillation but a fp16/bf16-based v2 would likely be stronger.
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- **Language coverage**: the training dataset is predominantly English; multilingual reasoning quality may vary by language.
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- **Multimodality**: the base architecture is `Mistral3` (vision + text). This LoRA is purely text-only training; vision capabilities are inherited unchanged from the base and have not been fine-tuned here.
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## Attribution & Acknowledgements
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- **[Mistral AI](https://mistral.ai/)** — for releasing an excellent multilingual base model under Apache 2.0.
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- **[OpenThoughts team](https://huggingface.co/open-thoughts)** — for assembling and releasing the high-quality reasoning distillation dataset.
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- **[DeepSeek](https://www.deepseek.com/)** — for the R1 model whose generations form the reasoning traces inside OpenThoughts-114k.
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- **[mlx-community](https://huggingface.co/mlx-community)** — for the MLX 4-bit quantization of the base model used as the LoRA host.
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- **[Apple ML Research](https://github.com/ml-explore/mlx-examples)** — for `mlx-lm`, which made training on a 64 GB Mac feasible.
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## Citation
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```bibtex
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@misc{pnoid_mistral_openthoughts_distill_v1_mlx_4bit_2026,
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author = {pnoid},
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title = {Mistral-Small-3.2-24B-OpenThoughts-Distill-v1 (MLX 4-bit)},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/pnoid/Mistral-Small-3.2-24B-OpenThoughts-Distill-v1-mlx-4bit}}
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}
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```
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When using this model, please also cite the base model and the dataset (see their respective model/dataset cards for canonical citation formats).
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---
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*Trained June 2026 on an Apple M4 Pro (64 GB unified memory).*
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chat_template.jinja
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{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST]' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}
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{
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"architectures": [
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"Mistral3ForConditionalGeneration"
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],
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"image_token_index": 10,
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"model_type": "mistral3",
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"multimodal_projector_bias": false,
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"projector_hidden_act": "gelu",
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"quantization": {
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"group_size": 64,
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"bits": 4
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},
|
| 13 |
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"quantization_config": {
|
| 14 |
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|
| 15 |
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|
| 16 |
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| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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"head_dim": 128,
|
| 21 |
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"hidden_act": "silu",
|
| 22 |
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"hidden_size": 5120,
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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"vocab_size": 131072,
|
| 35 |
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"tie_word_embeddings": false
|
| 36 |
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},
|
| 37 |
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"torch_dtype": "bfloat16",
|
| 38 |
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"transformers_version": "4.52.4",
|
| 39 |
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"vision_feature_layer": -1
|
| 40 |
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}
|
generation_config.json
ADDED
|
@@ -0,0 +1,7 @@
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|
| 1 |
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{
|
| 2 |
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"_from_model_config": true,
|
| 3 |
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"bos_token_id": 1,
|
| 4 |
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"eos_token_id": 2,
|
| 5 |
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"transformers_version": "4.52.4",
|
| 6 |
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"temperature": 0.15,
|
| 7 |
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}
|
model-00001-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 5285177419
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model-00002-of-00003.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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size 5314713098
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model-00003-of-00003.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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size 2660302214
|
model.safetensors.index.json
ADDED
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@@ -0,0 +1,935 @@
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tokenizer_config.json
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"use_default_system_prompt": false
|
| 14 |
+
}
|