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	---
	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
	---

	# NOTE: We've now launched Phind-CodeLlama-34B-v2, which acheives 73.8% pass@1 on HumanEval. It is instruction-tuned and much easier to use than this v1 model.
	# Check out Phind-CodeLlama-34B-v2 [here](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2).

	## 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
	```

	## How to Prompt the Model
	Please note that this model is somewhat instruction-tuned, but not chat-tuned.

	Do not try to use the Llama chat markup with this model. Instead, simply tell it what you want and add "\n: " at the end of your task.

	For example:

	```
	Write me a linked list implementation: \n
	```

	## How to reproduce HumanEval Results

	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