Triangle104/Llama-3.1-Tulu-3-8B-Q5_K_M-GGUF

This model was converted to GGUF format from allenai/Llama-3.1-Tulu-3-8B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.

Model description

Model type: A model trained on a mix of publicly available, synthetic and human-created datasets. Language(s) (NLP): Primarily English License: Llama 3.1 Community License Agreement Finetuned from model: allenai/Llama-3.1-Tulu-3-8B-DPO

Model Sources

Training Repository: https://github.com/allenai/open-instruct Eval Repository: https://github.com/allenai/olmes Paper: https://arxiv.org/abs/2411.15124 Demo: https://playground.allenai.org/

Using the model

Loading with HuggingFace

To load the model with HuggingFace, use the following snippet:

from transformers import AutoModelForCausalLM

tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3-8B")

VLLM

As a Llama base model, the model can be easily served with:

vllm serve allenai/Llama-3.1-Tulu-3-8B

Note that given the long chat template of Llama, you may want to use --max_model_len=8192.

Chat template

The chat template for our models is formatted as:

<|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

Or with new lines expanded:

<|user|> How are you doing? <|assistant|> I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

It is embedded within the tokenizer as well, for tokenizer.apply_chat_template.

System prompt

In Ai2 demos, we use this system prompt by default:

You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI.

The model has not been trained with a specific system prompt in mind.

Bias, Risks, and Limitations

The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.

Hyperparamters

PPO settings for RLVR:

Learning Rate: 3 × 10⁻⁷ Discount Factor (gamma): 1.0 General Advantage Estimation (lambda): 0.95 Mini-batches (N_mb): 1 PPO Update Iterations (K): 4 PPO's Clipping Coefficient (epsilon): 0.2 Value Function Coefficient (c1): 0.1 Gradient Norm Threshold: 1.0 Learning Rate Schedule: Linear Generation Temperature: 1.0 Batch Size (effective): 512 Max Token Length: 2,048 Max Prompt Token Length: 2,048 Penalty Reward Value for Responses without an EOS Token: -10.0 Response Length: 1,024 (but 2,048 for MATH) Total Episodes: 100,000 KL penalty coefficient (beta): [0.1, 0.05, 0.03, 0.01] Warm up ratio (omega): 0.0

License and use

All Llama 3.1 Tülu3 models are released under Meta's Llama 3.1 Community License Agreement. Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. Tülu3 is intended for research and educational use. For more information, please see our Responsible Use Guidelines.

The models have been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms: Gemma Terms of Use and Qwen License Agreement (models were improved using Qwen 2.5).

Citation

If Tülu3 or any of the related materials were helpful to your work, please cite:

@article{lambert2024tulu3, title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training}, author = { Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi }, year = {2024}, email = {tulu@allenai.org} }

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q5_K_M-GGUF --hf-file llama-3.1-tulu-3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q5_K_M-GGUF --hf-file llama-3.1-tulu-3-8b-q5_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q5_K_M-GGUF --hf-file llama-3.1-tulu-3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q5_K_M-GGUF --hf-file llama-3.1-tulu-3-8b-q5_k_m.gguf -c 2048