Tulu‑2‑DPO‑70B

The Apex of Preference‑Tuned Open Chat Models

What is Tulu‑2‑DPO‑70B?

Tulu‑2‑DPO‑70B is a 70‑billion‑parameter LLaMA‑2 model, crafted by the Allen Institute and optimized with Direct Preference Optimization (DPO) on a mixture of high-quality instruction datasets. As the top-end variant in the Tulu‑2 family, this model achieves exceptional alignment and conversational quality, consistently outperforming its 13B and 7B siblings, and surpassing many closed-source chat models (Hugging Face, Allen Institute for AI).

Key Features of Tulu‑2‑DPO‑70B

70B DPO-Finetuned Transformer

Based on LLaMA‑2, this massive model is optimized for nuanced reasoning, rich dialogue, code, and math capabilities through preference-aligned tuning (Hugging Face, Allen Institute for AI).

State‑of‑the‑Art Benchmarks

With an MT‑Bench score of 7.89, Tulu‑2‑DPO‑70B ranks as the highest-performing open model to date. It also boasts a 95.1% win rate on AlpacaEval, showcasing exceptional instruction alignment and response quality, validated by benchmarks like arXiv and Dataloop.

Optimized Input Format

Designed for the <|user|> … <|assistant|> structure, this ensures better output quality when used correctly (Hugging Face).

Quantized for Practical Deployment

Available in GGUF, GPTQ, and other optimized formats for llama.cpp, text-generation-webui, and more. Efficient quantization allows deployment with 30–50 GB RAM/VRAM (Hugging Face).

Low‑Risk License

Released under the AI2 ImpACT Low‑Risk license, suitable for research and internal use with clear reuse terms (Hugging Face).

Use Cases of Tulu‑2‑DPO‑70B

Build advanced dialogues and agents that closely follow user intent with minimal hallucination.

Ideal for code generation, reasoning, chain-of-thought logic, and structured multi-step problems.

Use in summarization, document understanding, tutoring systems, and AI-powered customer support.

Deploy and serve from local or cloud GPU servers using optimized formats, no vendor lock-in.

Serve as a research baseline for preference‑tuning, multi-task evaluation, and instruction formats.

Tulu‑2‑DPO‑70B

vs

Other Open Models

Model	MT-Bench	AlpacaEval	Tuning Method	Quant Support
Tulu-2-DPO-70B	7.89	95.1%	DPO over SFT	GGUF, GPTQ, more
Tulu-2-SFT-70B	7.49	86.6%	Supervised fine tuning	Same formats
Tulu-2-DPO-13B	7.00	89.5%	DPO over 13B	Similar formats
LLaMA-2 Chat-70B	~6.5	~70-75%	Meta RLHF	GGUF, GPTQ