Update README.md

README.md

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