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: 73.8%
verified: false
tags:
- code llama
- lmdeploy
This is Phind/Phind-CodeLlama-34B-v2 quantized to LMDeploy 4bit AWQ with the following config:
python3 -m lmdeploy.lite.apis.auto_awq \
--model ./Phind-CodeLlama-34B-v2 \
--w_bits 4 \
--w_group_size 128 \
--work_dir ./quant
Original Model Card:
Phind-CodeLlama-34B-v2
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.
Model Details
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.
Dataset Details
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.
How to Get Started with the Model
Make sure to install Transformers from the main git branch:
pip install git+https://github.com/huggingface/transformers.git
How to Prompt the Model
This model accepts the Alpaca/Vicuna instruction format.
For example:
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
How to reproduce HumanEval Results
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)[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 model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
Training details
- Hardware Type: 32x A100-80GB
- Hours used: 480 GPU-hours
- Cloud Provider: AWS
- Compute Region: us-east-1