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.
Furthermore, this model is instruction-tuned on the Alpaca/Vicuna format to be steerable and easy-to-use.
More details can be found on our blog post.
This model is fine-tuned from Phind-CodeLlama-34B-v1 and achieves 73.8% pass@1 on HumanEval.
Phind-CodeLlama-34B-v2 is multi-lingual and is proficient in Python, C/C++, TypeScript, Java, and more.
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.
Make sure to install Transformers from the main git branch:
pip install git+https://github.com/huggingface/transformers.git
This model accepts the Alpaca/Vicuna instruction format.
### System Prompt You are an intelligent programming assistant. ### User Message Implement a linked list in C++ ### Assistant ...
To reproduce our results:
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-v2" 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=384, 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) completion = completion.replace(prompt, "").split("\n\n\n") 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
This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
- Hardware Type: 32x A100-80GB
- Hours used: 480 GPU-hours
- Cloud Provider: AWS
- Compute Region: us-east-1
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