Create README.md

README.md

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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: 69.5%
+      verified: false
+tags:
+- code llama
+---
+
+Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)   
+_8-bit / fp4 / float16_  
+-_Mediocre_ 🥱
+
+
+# **Phind-CodeLlama-34B-Python-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-Python and achieves 69.5% 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