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--- |
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library_name: transformers |
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license: llama3 |
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datasets: |
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- aqua_rat |
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- microsoft/orca-math-word-problems-200k |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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--- |
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# Smaug-Llama-3-70B-Instruct |
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### Built with Meta Llama 3 |
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This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to |
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[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct). |
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The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below). |
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EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below. |
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We are conducting additional benchmark evaluations and will add those when available. |
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### Model Description |
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- **Developed by:** [Abacus.AI](https://abacus.ai) |
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- **License:** https://llama.meta.com/llama3/license/ |
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- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct). |
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## How to use |
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The prompt format is unchanged from Llama 3 70B Instruct. |
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### Use with transformers |
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See the snippet below for usage with Transformers: |
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```python |
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import transformers |
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import torch |
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model_id = "abacusai/Smaug-Llama-3-70B-Instruct" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device_map="auto", |
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) |
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messages = [ |
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, |
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{"role": "user", "content": "Who are you?"}, |
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] |
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prompt = pipeline.tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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terminators = [ |
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pipeline.tokenizer.eos_token_id, |
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=256, |
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eos_token_id=terminators, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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print(outputs[0]["generated_text"][len(prompt):]) |
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``` |
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## Evaluation |
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### Arena-Hard |
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Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge)) |
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| Model | Score | 95% Confidence Interval | Average Tokens | |
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| :---- | ---------: | ----------: | ------: | |
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| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 | |
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| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 | |
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| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 | |
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| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 | |
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| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 | |
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| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 | |
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| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 | |
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| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 | |
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| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 | |
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| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 | |
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| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 | |
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| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 | |
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| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 | |
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### MT-Bench |
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``` |
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########## First turn ########## |
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score |
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model turn |
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Smaug-Llama-3-70B-Instruct 1 9.40000 |
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GPT-4-Turbo 1 9.37500 |
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Meta-Llama-3-70B-Instruct 1 9.21250 |
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########## Second turn ########## |
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score |
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model turn |
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Smaug-Llama-3-70B-Instruct 2 9.0125 |
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GPT-4-Turbo 2 9.0000 |
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Meta-Llama-3-70B-Instruct 2 8.8000 |
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########## Average ########## |
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score |
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model |
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Smaug-Llama-3-70B-Instruct 9.206250 |
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GPT-4-Turbo 9.187500 |
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Meta-Llama-3-70B-Instruct 9.006250 |
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``` |
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| Model | First turn | Second Turn | Average | |
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| :---- | ---------: | ----------: | ------: | |
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| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 | |
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| GPT-4-Turbo | 9.38 | 9.00 | 9.19 | |
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| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 | |
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This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
