73 lines
2.0 KiB
Markdown
73 lines
2.0 KiB
Markdown
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---
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tags:
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- text-generation-inference
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- transformers
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- qwen2
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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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# sf-100
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Conversational fine-tune of Qwen2.5-7B-Instruct, supervised-fine-tuned Hugging Face TRL.
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## Model details
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- **Architecture:** Qwen2 (7B)
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- **Parameters:** ~7.6B (reported as 8B in repo metadata)
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- **Precision:** BF16 merged weights, trained on top of a 4-bit bnb-quantized base
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- **License:** Apache-2.0
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- **Language:** Multi
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- **Developed by:** liamka
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## Intended use
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General-purpose conversational assistant — single- and multi-turn chat.
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**Not suitable for** safety-critical settings (medical, legal, financial advice), non-English input (not evaluated), or
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high-stakes factual lookup without verification.
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "liamka/sf-100"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Hi, who are you?"}]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
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print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Training
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- **Framework:** Unsloth + TRL (`SFTTrainer`)
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- **Method:** Supervised fine-tuning on top of Qwen2.5-7B-Instruct
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- **No RLHF / DPO** applied
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Dataset, step count and hyperparameters are not published.
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## Limitations
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- Inherits biases and knowledge cutoff from Qwen2.5-7B-Instruct.
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- SFT only — no preference optimisation, so safety and refusal behaviour matches the base or weaker.
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- Can hallucinate. Verify factual claims.
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- Evaluated only informally; no benchmark numbers reported.
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## Acknowledgements
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- [Qwen2.5](https://huggingface.co/Qwen) — Alibaba
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- [TRL](https://github.com/huggingface/trl) — Hugging Face
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