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README.md
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@@ -6,7 +6,6 @@ language:
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- en
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base_model:
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- unsloth/phi-4-reasoning
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new_version: SkyAsl/Rust-Master-thinking
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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model_id = "SkyAsl/Rust-Master-thinking"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id,
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model.eval()
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prompt = "Explain why Rust ownership prevents data races."
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input_text = (
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f"<|user|>\n{
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f"<|assistant|>\n<think>\n"
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)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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This dataset improves the model's ability to produce structured and
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accurate explanations for Rust programming tasks.
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## 🔍 Notes on Reasoning Tags
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This model preserves **hidden reasoning structure**:
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- `<think>` content is **internal chain-of-thought**
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- The final output is **placed after the reasoning block**
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⚠️ Users should NOT expect the `<think>` content to be revealed; the
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model is aligned to hide reasoning by default.
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## ✨ Acknowledgements
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- **Unsloth** for optimized model training
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- en
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base_model:
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- unsloth/phi-4-reasoning
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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model_id = "SkyAsl/Rust-Master-thinking"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
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model.eval()
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prompt = "Explain why Rust ownership prevents data races."
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input_text = (
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f"<|user|>\n{prompt}\n"
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f"<|assistant|>\n<think>\n"
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)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=3000,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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This dataset improves the model's ability to produce structured and
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accurate explanations for Rust programming tasks.
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## ✨ Acknowledgements
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- **Unsloth** for optimized model training
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