|
--- |
|
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: 67.6% |
|
verified: false |
|
tags: |
|
- code llama |
|
--- |
|
|
|
# **Phind-CodeLlama-34B-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 and achieves 67.6% 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 |
|
``` |
|
|
|
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 |
