Attribute	Value
Base Model	meta-llama/Llama-3.1-8B
Training Method	Supervised Fine-Tuning (SFT)
Dataset	allenai/tulu-3-sft-mixture (10%)
Framework	allenai/open-instruct
License	Llama 3.1 Community 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

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, 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 framework.