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README.md

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+---
+license: apache-2.0
+base_model: meta-llama/Llama-2-7b-hf
+tags:
+- llama
+- lora
+- instruction-tuning
+- dolly
+- peft
+datasets:
+- databricks/databricks-dolly-15k
+language:
+- en
+---
+
+# LLaMA 2.7B Fine-tuned on Dolly
+
+This is a LoRA adapter for LLaMA-2.7B, fine-tuned on the Databricks Dolly dataset for instruction-following tasks.
+
+## Model Details
+
+- **Base Model**: LLaMA-2.7B
+- **Training Method**: LoRA (Low-Rank Adaptation)
+- **Dataset**: Databricks Dolly 15k
+- **Adapter Type**: PEFT LoRA
+
+## LoRA Configuration
+
+- **Rank (r)**: 16
+- **Alpha**: 32
+- **Dropout**: 0.05
+- **Target Modules**: Query and Value projection layers
+- **Trainable Parameters**: ~8-16M (adapters only, <1% of base model)
+
+## Training Configuration
+
+- **Epochs**: 5
+- **Batch Size**: 4
+- **Learning Rate**: 5e-04
+- **Gradient Accumulation**: 1
+- **GPUs**: 2
+- **Training Steps**: 6810
+- **Optimizer**: AdamW
+- **Weight Decay**: 0.01
+
+## Usage
+
+You need to install the required packages:
+
+```bash
+pip install transformers peft torch
+```
+
+Then load and use the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model (replace with actual 2.7B base model)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b-hf",  # Update to 2.7B base if available
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(
+    base_model,
+    "YOUR_USERNAME/llama-2.7b-fine-tuned-on-dolly"
+)
+
+# Optional: Merge adapter for faster inference
+# model = model.merge_and_unload()
+
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/llama-2.7b-fine-tuned-on-dolly")
+
+# Generate
+prompt = "Instruction: Write a short poem about AI.\n\nResponse:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_length=256,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Key Benefits
+
+- **Efficiency**: Only ~8-16M trainable parameters (vs billions in full fine-tuning)
+- **Storage**: Small adapter files (~30-60MB vs multi-GB full models)
+- **Modularity**: Can swap adapters on the same base model
+- **Quality**: Maintains competitive performance with full fine-tuning
+
+## Limitations
+
+- Requires the base LLaMA model to use
+- Performance depends on base model quality
+- Trained primarily on English instruction-following tasks
+- May generate biased or incorrect responses
+
+## Training Details
+
+This model was fine-tuned using:
+- **PEFT/LoRA**: Parameter-efficient fine-tuning
+- **Training Data**: 15k instruction-response pairs from Dolly
+- **Task**: General instruction following and question answering
+- **Learning Rate Schedule**: Cosine decay with warmup
+
+## Citation
+
+```bibtex
+@inproceedings{lora,
+  title={LoRA: Low-Rank Adaptation of Large Language Models},
+  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
+  booktitle={International Conference on Learning Representations},
+  year={2022}
+}
+```
+
+## License
+
+This model is released under Apache 2.0 license. Note that LLaMA models have specific usage terms from Meta.