| --- |
| license: llama2 |
| model-index: |
| - name: Phind-CodeLlama-34B-v1 |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| type: openai_humaneval |
| name: HumanEval |
| metrics: |
| - name: pass@1 |
| type: pass@1 |
| value: 69.5% |
| verified: false |
| tags: |
| - code llama |
| --- |
| |
| # **Phind-CodeLlama-34B-Python-v1** |
| We've fine-tuned CodeLlama-34B and CodeLlama-34B-Python on an internal Phind dataset that achieve 67.6% and 69.5% pass@1 on HumanEval, respectively. GPT-4 achieves 67%. We've applied OpenAI's decontamination methodology to our dataset to ensure result validity. |
|
|
| More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4). |
|
|
| ## Model Details |
| This model is fine-tuned from CodeLlama-34B-Python and achieves 69.5% pass@1 on HumanEval. |
|
|
| ## Dataset Details |
| We fined-tuned on a proprietary dataset of ~80k high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. The Phind models were trained for 2 epochs, for a total of ~160k examples shown. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in three hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens. |
|
|
| ## How to Get Started with the Model |
|
|
| Make sure to install Transformers from the main git branch: |
|
|
| ```bash |
| pip install git+https://github.com/huggingface/transformers.git |
| ``` |
|
|
| ## How to Prompt the Model |
| **Please note that this model is somewhat instruction-tuned, but not chat-tuned.** |
|
|
| Do not try to use the Llama chat markup with this model. Instead, simply tell it what you want and add "\n: " at the end of your task. |
|
|
| For example: |
|
|
| ``` |
| Write me a linked list implementation: \n |
| ``` |
|
|
| ## How to reproduce HumanEval Results |
|
|
| To reproduce our results: |
|
|
| ```python |
| |
| from transformers import AutoTokenizer, LlamaForCausalLM |
| from human_eval.data import write_jsonl, read_problems |
| from tqdm import tqdm |
| |
| # initialize the model |
| |
| model_path = "Phind/Phind-CodeLlama-34B-v1" |
| model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto") |
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
| |
| # HumanEval helper |
| |
| def generate_one_completion(prompt: str): |
| tokenizer.pad_token = tokenizer.eos_token |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096) |
| |
| # Generate |
| generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=256, do_sample=True, top_p=0.75, top_k=40, temperature=0.1) |
| completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
| completion = completion.replace(prompt, "").split("\n\n\n")[0] |
| |
| return completion |
| |
| # perform HumanEval |
| problems = read_problems() |
| |
| num_samples_per_task = 1 |
| samples = [ |
| dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) |
| for task_id in tqdm(problems) |
| for _ in range(num_samples_per_task) |
| ] |
| write_jsonl("samples.jsonl", samples) |
| |
| # run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox |
| ``` |
|
|
| ## Bias, Risks, and Limitations |
|
|
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
| This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments. |
|
|
|
|
| ## Training details |
|
|
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
|
|
| - **Hardware Type:** 32x A100-80GB |
| - **Hours used:** 90 GPU-hours |
| - **Cloud Provider:** AWS |
| - **Compute Region:** us-east-1 |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-Python-v1) |
|
|
| | Metric | Value | |
| |-----------------------|---------------------------| |
| | Avg. | 35.69 | |
| | ARC (25-shot) | 24.66 | |
| | HellaSwag (10-shot) | 29.77 | |
| | MMLU (5-shot) | 27.95 | |
| | TruthfulQA (0-shot) | 45.27 | |
| | Winogrande (5-shot) | 68.82 | |
| | GSM8K (5-shot) | 21.53 | |
| | DROP (3-shot) | 31.85 | |
|
|
