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--- |
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license: apache-2.0 |
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base_model: microsoft/phi-2 |
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tags: |
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- peft |
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- lora |
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- gsm8k |
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- math |
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- reasoning |
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datasets: |
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- gsm8k |
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metrics: |
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- accuracy |
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library_name: peft |
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--- |
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# phi-2 LoRA Adapter for GSM8K |
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This is a LoRA adapter for [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) fine-tuned on the [GSM8K dataset](https://huggingface.co/datasets/gsm8k) for mathematical reasoning. |
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## Model Description |
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- **Base Model:** microsoft/phi-2 |
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- **Training Method:** Baseline fine-tuning |
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- **Dataset:** GSM8K (Grade School Math 8K) |
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- **Task:** Mathematical word problem solving |
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- **Exact Match Accuracy:** 60.16% |
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## Training Details |
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LoRA adapter for PHI-2 fine-tuned on GSM8K with standard (baseline) training |
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### Training Configuration |
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- **Method:** LoRA (Low-Rank Adaptation) |
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- **Rank:** 16 |
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- **Alpha:** 32 |
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- **Target Modules:** q_proj, k_proj, v_proj, o_proj |
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- **Dropout:** 0.1 |
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- **Epochs:** 3 |
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- **Batch Size:** 4 (with gradient accumulation of 4) |
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- **Learning Rate:** 3e-4 |
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## Usage |
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### Loading the Adapter |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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# Load base model |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/phi-2", |
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device_map="auto", |
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torch_dtype="auto" |
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) |
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# Load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2") |
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# Load LoRA adapter |
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model = PeftModel.from_pretrained(base_model, "CrystalRaindropsFall/phi2-gsm8k-baseline") |
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# Inference |
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prompt = "Question: Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\nAnswer:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### Using with Pipeline |
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```python |
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from transformers import pipeline |
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from peft import PeftModel, AutoPeftModelForCausalLM |
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# Load model with adapter |
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model = AutoPeftModelForCausalLM.from_pretrained( |
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"YOUR_USERNAME/REPO_NAME", |
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device_map="auto" |
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) |
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# Create pipeline |
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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# Generate |
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result = pipe("Question: A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\nAnswer:") |
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print(result[0]['generated_text']) |
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``` |
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## Performance |
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Evaluated on GSM8K test set (512 samples): |
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| Metric | Score | |
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|--------|-------| |
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| Exact Match | 60.16% | |
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| Format Correct | 100% | |
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## Limitations |
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- Trained on grade school level math problems |
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- May struggle with problems requiring external knowledge |
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- Performance depends on problem complexity and wording |
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- Best used with base model's standard generation settings |
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## Acknowledgments |
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- Base model: microsoft/phi-2 |
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- Dataset: GSM8K by Cobbe et al. |
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- Training framework: HuggingFace PEFT |
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## License |
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Apache 2.0 (following base model license) |
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