README.md

---
license: llama3.1
language:
- en
pipeline_tag: text-generation
datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
base_model: allenai/Llama-3.1-Tulu-3-8B
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-3.1-Tulu-3-8B-Q6_K-GGUF
This model was converted to GGUF format from [`allenai/Llama-3.1-Tulu-3-8B`](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) for more details on the model.

---
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 = {[email protected]}
}

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q6_K-GGUF --hf-file llama-3.1-tulu-3-8b-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q6_K-GGUF --hf-file llama-3.1-tulu-3-8b-q6_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q6_K-GGUF --hf-file llama-3.1-tulu-3-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-3.1-Tulu-3-8B-Q6_K-GGUF --hf-file llama-3.1-tulu-3-8b-q6_k.gguf -c 2048
```