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README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B
+library_name: transformers
+tags:
+- llama
+- sft
+- tulu
+- mahals
+- alignment
+datasets:
+- allenai/tulu-3-sft-mixture
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Llama-3.1-8B-Tulu10pct-SFT-MAHALS
+
+Supervised Fine-Tuned (SFT) Llama 3.1 8B model trained on 10% of the Tulu-3 SFT mixture for the MAHALS research project.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) |
+| **Training Method** | Supervised Fine-Tuning (SFT) |
+| **Dataset** | [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) (10%) |
+| **Framework** | [allenai/open-instruct](https://github.com/allenai/open-instruct) |
+| **License** | [Llama 3.1 Community License](https://llama.meta.com/llama3_1/license/) |
+
+## Inference Requirements
+
+| Precision | VRAM Required | Compatible GPUs |
+|-----------|---------------|-----------------|
+| BF16/FP16 | ~20 GB | A100 40GB, RTX 4090/3090, A10 |
+| INT8 | ~10 GB | T4, RTX 3080 |
+| INT4 | ~6 GB | RTX 3060, consumer GPUs |
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "anonymousML123/Llama-3.1-8B-Tulu10pct-SFT-MAHALS"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [{"role": "user", "content": "What is machine learning?"}]
+
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### vLLM Serving
+
+```bash
+vllm serve anonymousML123/Llama-3.1-8B-Tulu10pct-SFT-MAHALS --max_model_len=4096
+```
+
+## Chat Template
+
+This model uses the Tulu chat template:
+
+```
+<|user|>
+Your question here
+<|assistant|>
+Model response<|endoftext|>
+```
+
+## Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 5e-6 |
+| Effective Batch Size | 128 |
+| Gradient Accumulation | 16 |
+| Max Sequence Length | 4096 |
+| Epochs | 2 |
+| LR Schedule | Linear |
+| Warmup Ratio | 0.03 |
+| Optimizer | AdamW |
+| Precision | BF16 |
+
+## Training Data
+
+10% random sample (~94K examples) from [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), which includes:
+- FLAN v2
+- Open Assistant
+- ShareGPT
+- Code instructions
+- Math instructions
+
+## Intended Use
+
+This model is intended for research on multi-agent alignment and instruction following. It is part of the MAHALS (Multi-Agent Hierarchical Alignment) research project.
+
+## Limitations
+
+- Trained on 10% of data (reduced capability vs full Tulu-3)
+- English only
+- May exhibit biases present in training data
+- Not suitable for production without further evaluation
+
+## Citation
+
+```bibtex
+@misc{mahals2026,
+  title={MAHALS: Multi-Agent Hierarchical Alignment},
+  author={Anonymous},
+  year={2026},
+  note={Under review}
+}
+```
+
+## Acknowledgments
+
+Built using [AllenAI's open-instruct](https://github.com/allenai/open-instruct) framework.