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README.md
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---
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license: llama2
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---
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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: 69.5%
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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-Python-v1**
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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.
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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 CodeLlama-34B-Python and achieves 69.5% pass@1 on HumanEval.
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## Dataset Details
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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.
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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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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-v1"
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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=256, 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:** 90 GPU-hours
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- **Cloud Provider:** AWS
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- **Compute Region:** us-east-1
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