45 lines
1.5 KiB
Markdown
45 lines
1.5 KiB
Markdown
---
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base_model: breitburg/pure-7b-210726
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- reasoning
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- chain-of-thought
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license: apache-2.0
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language:
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- en
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---
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# pure-reasoning-7b-230726
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A reasoning ("thinking") fine-tune of [breitburg/pure-7b-210726](https://huggingface.co/breitburg/pure-7b-210726).
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Given a chat prompt it first produces a `<think>...</think>` reasoning block, then commits to an
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answer, and stops on `<|im_end|>`.
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- **Developed by:** breitburg
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- **License:** apache-2.0
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- **Finetuned from:** breitburg/pure-7b-210726
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- **Dataset:** [breitburg/reasonable-chats](https://huggingface.co/datasets/breitburg/reasonable-chats)
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- **Date:** 23 July 2026
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- **Method:** LoRA SFT (Unsloth + TRL). Two new vocab tokens `<think>`/`</think>` were added and
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trained full-rank on `embed_tokens`/`lm_head`; the chat template folds the dataset's per-turn
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reasoning traces into `<think>` blocks.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("breitburg/pure-reasoning-7b-230726")
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model = AutoModelForCausalLM.from_pretrained("breitburg/pure-reasoning-7b-230726", device_map="cuda")
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msgs = [{"role": "user", "content": "What causes the seasons on Earth?"}]
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inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
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out = model.generate(inputs, max_new_tokens=300)
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print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=False))
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```
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Trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth).
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