PKU-Baichuan-MLSystemLab
/

Llama3-PBM-Nova-70B

@@ -1,199 +1,69 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- Chat Model
+- SFT
+- RLHF
+license: llama3
 ---
+# Llama3-PBM-Nova-70B
+## Introduction
+Llama3-PBM-Nova-70B is a chat model developed by PKU-Baichuan-MLSysLab, based on the Llama3-70B. In order to better utilize open-source data, we've performed deduplication, quality filtering, and data synthesis on it. Then, through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), we've significantly enhanced the base model's performance.
+- **Developed by:** [PKU-Baichuan-MLSysLab](https://github.com/PKU-Baichuan-MLSystemLab)
+- **Base Model:** [Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
+- **Model Type:** Chat Model
+- **Training Method:** SFT + RLHF
+- **Release Date:** August 2024
 ## Evaluation
+| Model                  | Arena-Hard | MixEval-Hard | Alpaca-Eval 2.0 |
+|------------------------|------------|--------------|-----------------|
+| GPT-4Turbo(04/09)      | 82.6%      | 62.6         | 55.0%           |
+| GPT-4o(05/13)          | 79.2%      | 64.7         | 57.5%           |
+| Gemini 1.5 Pro         | 72.0%      | 58.3         | -               |
+| Llama3-PBM-Nova-70B    | 74.5%      | 58.1         | 61.23%          |
+| Llama-3.1-70B-Instruct | 55.7%      | -            | 38.1%           |
+| Llama-3-70B-Instruct   | 46.6       | 55.9         | 34.4%           |
+## Usage
+Below is an example of how to use this model based on the Transformers library.
+```
+import transformers
+import torch
+model_id = "PKU-Baichuan-MLSystemLab/Llama3-PBM-Nova-70B"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+print(outputs[0]["generated_text"][-1])
+```