# Phi-2 Coder 👩‍💻 **Phi-2** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library. ## Model description 🧠 [Phi-2](https://huggingface.co/microsoft/phi-2) Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters. ## Training and evaluation data 📚 [CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model. ### Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 66 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7631 | 0.36 | 50 | 0.7174 | | 0.6735 | 0.71 | 100 | 0.6949 | | 0.696 | 1.07 | 150 | 0.6893 | | 0.7861 | 1.42 | 200 | 0.6875 | | 0.7346 | 1.78 | 250 | 0.6867 | ### HumanEval results 📊 WIP ### Example of usage 👩‍💻 ```py import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mrm8488/phi-2-coder" tokenizer = AutoTokenizer.from_pretrained(model_id, add_bos_token=True, trust_remote_code=True, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, device="auto") def generate( instruction, max_new_tokens=128, temperature=0.1, top_p=0.75, top_k=40, num_beams=2, **kwargs, ): prompt = "Instruct: " + instruction + "\nOutput:" print(prompt) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to("cuda") attention_mask = inputs["attention_mask"].to("cuda") with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, eos_token_id = tokenizer.eos_token_id, use_cache=True, early_stopping=True ) output = tokenizer.decode(generation_output[0]) return output.split("\nOutput:")[1].lstrip("\n") instruction = "Design a class for representing a person in Python." print(generate(instruction)) ``` ### How to use with [MLX](https://github.com/ml-explore/mlx). ```bash # Install mlx, mlx-examples, huggingface-cli pip install mlx pip install huggingface_hub hf_transfer git clone https://github.com/ml-explore/mlx-examples.git # Download model export HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download --local-dir phi-2-coder mrm8488/phi-2-coder # Run example python mlx-examples/llms/phi2.py --model-path phi-2-coder --prompt "Design a class for representing a person in Python" ``` ### Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { phi-2-coder (Revision 4ae69ae) }, year = 2023, url = { https://huggingface.co/mrm8488/phi-2-coder }, doi = { 10.57967/hf/1518 }, publisher = { Hugging Face } } ```