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--- |
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license: llama2 |
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model-index: |
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- name: Phind-CodeLlama-34B-v1 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: openai_humaneval |
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name: HumanEval |
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metrics: |
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- name: pass@1 |
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type: pass@1 |
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value: 73.8% |
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verified: false |
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tags: |
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- code llama |
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--- |
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# **Phind-CodeLlama-34B-v2** |
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We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1.5B tokens high-quality programming-related data, achieving **73.8% pass@1** on HumanEval. It's the current state-of-the-art amongst open-source models. |
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Furthermore, this model is **instruction-tuned** on the Alpaca/Vicuna format to be steerable and easy-to-use. |
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More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4). |
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## Model Details |
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This model is fine-tuned from Phind-CodeLlama-34B-v1 and achieves **73.8% pass@1** on HumanEval. |
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Phind-CodeLlama-34B-v2 is **multi-lingual** and is proficient in Python, C/C++, TypeScript, Java, and more. |
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## Dataset Details |
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We fined-tuned on a proprietary dataset of 1.5B tokens of high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in 15 hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens. |
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## How to Get Started with the Model |
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Make sure to install Transformers from the main git branch: |
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```bash |
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pip install git+https://github.com/huggingface/transformers.git |
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``` |
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## How to Prompt the Model |
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This model accepts the Alpaca/Vicuna instruction format. |
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For example: |
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``` |
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### System Prompt |
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You are an intelligent programming assistant. |
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### User Message |
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Implement a linked list in C++ |
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### Assistant |
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... |
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``` |
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## How to reproduce HumanEval Results |
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To reproduce our results: |
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```python |
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from transformers import AutoTokenizer, LlamaForCausalLM |
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from human_eval.data import write_jsonl, read_problems |
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from tqdm import tqdm |
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# initialize the model |
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model_path = "Phind/Phind-CodeLlama-34B-v2" |
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model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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# HumanEval helper |
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def generate_one_completion(prompt: str): |
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tokenizer.pad_token = tokenizer.eos_token |
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096) |
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# Generate |
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generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1) |
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completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
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completion = completion.replace(prompt, "").split("\n\n\n")[0] |
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return completion |
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# perform HumanEval |
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problems = read_problems() |
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num_samples_per_task = 1 |
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samples = [ |
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dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) |
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for task_id in tqdm(problems) |
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for _ in range(num_samples_per_task) |
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] |
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write_jsonl("samples.jsonl", samples) |
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# run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox |
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``` |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments. |
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## Training details |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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- **Hardware Type:** 32x A100-80GB |
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- **Hours used:** 480 GPU-hours |
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- **Cloud Provider:** AWS |
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- **Compute Region:** us-east-1 |
