Merge branch 'main' of hf.co:Phind/Phind-CodeLlama-34B-v2 into main

README.md

@@ -1,3 +1,115 @@
 ---
 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
 ---
+
+# **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](https://www.phind.com/blog/code-llama-beats-gpt4).
+
+## 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:
+
+```bash
+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:
+
+```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-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 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:** 480 GPU-hours
+- **Cloud Provider:** AWS
+- **Compute Region:** us-east-1