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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ---
+base_model: allenai/Llama-3.1-Tulu-3-8B
+language:
+- en
 library_name: transformers
+license: llama3.1
+tags:
+- llama-3
+- llama
+- meta
+- facebook
+- unsloth
+- transformers
 ---
+# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
+We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# unsloth/Llama-3.1-Tulu-3-8B-bnb-4bit
+For more details on the model, please go to Allen AI's original [model card](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B)
+## ✨ Finetune for Free
+All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
+| **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing)               | 2x faster | 60% less |
+| **Qwen2 VL (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1whHb54GNZMrNxIsi2wm2EY_-Pvo2QyKh?usp=sharing)               | 1.8x faster | 60% less |
+| **Qwen2.5 (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing)               | 2x faster | 60% less |
+| **Llama-3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
+| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
+| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |
+| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
+| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai)
+- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
+- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
+- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
+<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu3/Tulu3-logo.png" alt="Tulu 3 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+# Llama-3.1-Tulu-3-8B
+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://allenai.org/papers/tulu-3-report.pdf (arXiv soon)
+- **Demo:** https://playground.allenai.org/
+### Model Family
+| **Stage**           | **Llama 3.1 8B**                                                                                          | **Llama 3.1 70B**                                                                                         |
+|----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
+| **Base Model**       | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)                                | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B)                              |
+| **SFT**              | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT)                | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT)              |
+| **DPO**              | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO)                | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO)              |
+| **Final Models (RLVR)**     | [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B)                        | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B)                      |
+| **Reward Model (RM)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM)                                                     | (Same as 8B)                                                     |
+## 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.
+## Performance
+| Benchmark (eval)                | Tülu 3 SFT 8B | Tülu 3 DPO 8B | Tülu 3 8B | Llama 3.1 8B Instruct | Qwen 2.5 7B Instruct | Magpie 8B | Gemma 2 9B Instruct | Ministral 8B Instruct |
+|---------------------------------|----------------|----------------|------------|------------------------|----------------------|-----------|---------------------|-----------------------|
+| **Avg.**                        | 60.4           | 64.4           | **64.8**   | 62.2                  | 57.8                | 44.7      | 55.2               | 58.3                 |
+| **MMLU (0 shot, CoT)**          | 65.9           | 68.7           | 68.2       | 71.2                  | **76.6**            | 62.0      | 74.6               | 68.5                 |
+| **PopQA (15 shot)**             | **29.3**       | 29.3           | 29.1       | 20.2                  | 18.1                | 22.5      | 28.3               | 20.2                 |
+| **TruthfulQA (6 shot)**         | 46.8           | 56.1           | 55.0       | 55.1                  | **63.1**            | 57.0      | 61.4               | 55.5                 |
+| **BigBenchHard (3 shot, CoT)**  | **67.9**       | 65.8           | 66.0       | 62.8                  | 21.7                | 0.9       | 2.5                | 56.2                 |
+| **DROP (3 shot)**               | 61.3           | 62.5           | **62.6**   | 61.5                  | 54.4                | 49.4      | 58.8               | 56.2                 |
+| **MATH (4 shot CoT, Flex)**     | 31.5           | 42.0           | **43.7**   | 42.5                  | 14.8                | 5.1       | 29.8               | 40.0                 |
+| **GSM8K (8 shot, CoT)**         | 76.2           | 84.3           | **87.6**   | 83.4                  | 83.8                | 61.2      | 79.7               | 80.0                 |
+| **HumanEval (pass@10)**         | 86.2           | 83.9           | 83.9       | 86.3                  | **93.1**            | 75.4      | 71.7               | 91.0                 |
+| **HumanEval+ (pass@10)**        | 81.4           | 78.6           | 79.2       | 82.9                  | **89.7**            | 69.1      | 67.0               | 88.5                 |
+| **IFEval (prompt loose)**       | 72.8           | 81.1           | **82.4**   | 80.6                  | 74.7                | 38.8      | 69.9               | 56.4                 |
+| **AlpacaEval 2 (LC % win)**     | 12.4           | 33.5           | 34.5       | 24.2                  | 29.0                | **49.0**  | 43.7               | 31.4                 |
+| **Safety (6 task avg.)**        | **93.1**       | 87.2           | 85.5       | 75.2                  | 75.0                | 46.4      | 75.5               | 56.2                 |
+| Benchmark (eval)                | Tülu 3 70B SFT | Tülu 3 DPO 70B | Tülu 3 70B | Llama 3.1 70B Instruct | Qwen 2.5 72B Instruct | Hermes 3 Llama 3.1 70B | Nemotron Llama 3.1 70B |
+|---------------------------------|-----------------|-----------------|-------------|-------------------------|-----------------------|------------------------|-------------------------|
+| **Avg.**                        | 72.6            | 75.9            | **76.0**    | 73.4                   | 71.5                  | 68.3                   | 65.5                   |
+| **MMLU (0 shot, CoT)**          | 78.9            | 83.3            | 83.1        | 85.3                   | **85.5**             | 80.4                   | 83.8                   |
+| **PopQA (15 shot)**             | **48.6**        | 46.3            | 46.5        | 46.4                   | 30.6                  | 48.1                   | 36.4                   |
+| **TruthfulQA (6 shot)**         | 55.7            | 67.9            | 67.6        | 66.8                   | **69.9**             | 66.5                   | 62.6                   |
+| **BigBenchHard (3 shot, CoT)**  | **82.7**        | 81.8            | 82.0        | 73.8                   | 67.2                  | 82.1                   | 0.7                    |
+| **DROP (3 shot)**               | **77.2**        | 74.1            | 74.3        | 77.0                   | 34.2                  | 73.2                   | 68.8                   |
+| **MATH (4 shot CoT, Flex)**     | 53.7            | 62.3            | 63.0        | 56.4                   | **74.3**             | 41.9                   | 55.0                   |
+| **GSM8K (8 shot, CoT)**         | 91.1            | 93.5            | 93.5        | **93.7**              | 89.5                  | 90.0                   | 84.7                   |
+| **HumanEval (pass@10)**         | 92.9            | 92.4            | 92.4        | 93.6                   | 94.0                  | 89.6                   | **94.1**              |
+| **HumanEval+ (pass@10)**        | 87.3            | 88.4            | 88.0        | 89.5                   | **90.8**             | 85.9                   | 85.5                   |
+| **IFEval (prompt loose)**       | 82.1            | 82.6            | 83.2        | **88.0**              | 87.6                  | 76.0                   | 79.9                   |
+| **AlpacaEval 2 (LC % win)**     | 26.3            | 49.6            | 49.8        | 33.4                   | 47.7                  | 28.4                   | **66.1**              |
+| **Safety (6 task avg.)**        | **94.4**        | 89.0            | 88.3        | 76.5                   | 87.0                  | 57.9                   | 69.0                   |
+## 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](https://www.llama.com/llama3_1/license/).
+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](https://allenai.org/responsible-use).
+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](https://ai.google.dev/gemma/terms) and [Qwen License Agreement](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE) (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]}
+}
+```