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---
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license: apache-2.0
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base_model:
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- mistralai/Mistral-7B-Instruct-v0.3
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datasets:
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- samzheng/SymbolicCode-CoT
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language:
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- en
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pipeline_tag: question-answering
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---
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model_name: "Mistral-7B-Math (Merged FP16 Checkpoint)" \ |
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repo: "samzheng/mistral-7b-math-merged"\ |
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base_model: |
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name: "unsloth/mistral-7b-instruct-v0.3-bnb-4bit" |
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url: "https://huggingface.co/unsloth/mistral-7b-instruct-v0.3-bnb-4bit"\ |
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task: "Grade-school symbolic math word problems → Python code answers"\ |
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fine_tuning: |
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method: "LoRA adapters (r=16, α=16, dropout=0) merged into the base weights, FP16 precision" |
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parameters: |
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r: 16 |
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alpha: 16 |
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dropout: 0\ |
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dataset: |
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description: "6.7k Alpaca-formatted Q/A pairs with chain-of-thought + code" |
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splits: |
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- "symboliccode_cot_train" |
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- "symboliccode_cot_validation"\ |
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language: python \ |
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code: |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained( |
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"samzheng/mistral-7b-math-merged", |
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torch_dtype="auto", device_map="auto" |
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) |
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tok = AutoTokenizer.from_pretrained("samzheng/mistral-7b-math-merged") |
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prompt = """Below is an instruction that describes a task... |
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### Instruction: Solve the problem using step-by-step reasoning and provide Python code. |
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### Input: Solve for x: 2x + 5 = 17 |
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### Response: |
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""" |
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print(tok.decode(model.generate(**tok(prompt, return_tensors="pt").to(model.device),max_new_tokens=256)[0], skip_special_tokens=True)) |
