OpenAssistant‑SFT‑7‑LLaMA‑30B

The Open Assistant at Flagship Scale

What is OpenAssistant‑SFT‑7‑LLaMA‑30B?

OpenAssistant‑SFT‑7‑LLaMA‑30B is a 30‑billion‑parameter large language model based on Meta’s LLaMA‑30B, fine‑tuned through supervised instruction training (SFT epoch 7) on the OpenAssistant Conversations dataset, which includes multilingual assisted dialogue that spans chat, code, math, and task completion (Hugging Face, promptlayer.com).

To respect licensing, the public release is distributed via an XOR‑weight scheme or GPTQ quantized binaries, allowing inference without redistributing original LLaMA weights (Dataloop).

Key Features of OpenAssistant‑SFT‑7‑LLaMA‑30B

30B‑Parameter Dense Transformer

Built on LLaMA‑30B, providing strong context understanding, reasoning, and multi-turn conversation capabilities.

Epoch‑7 Supervised Fine‑Tuning (SFT‑7)

Trained on multiple high-quality datasets, including OASST, Vicuna dialogs, Dolly‑15K, Code‑Alpaca, and grade‑school math instructions, yielding a versatile instruction‑following assistant (promptlayer.com, Hugging Face).

Multilingual & Task‑Diverse

Supports 20+ languages and specialized tasks like code generation and math reasoning, aligned with diverse AI uses (promptlayer.com).

XOR or GPTQ Quant for Private Use

Available in quantized formats (2‑6‑bit XOR or GPTQ‑4bit) to enable fast, RAM-efficient local inference via llama.cpp or AutoGPTQ tools (Hugging Face, Hugging Face).

Inference-Optimized Setup

With 4-bit quantization options requiring as little as ~17 GB RAM or 16 GB GPU VRAM, it’s accessible for high-end home rigs or cloud GPU servers (Reddit).

Use Cases of OpenAssistant‑SFT‑7‑LLaMA‑30B

Ideal for building assistants that follow detailed instructions, support conversation, and are more controllable than raw LLaMA.

Use with 20+ language support for benchmarking cross-language dialogue or agent studies.

Supports structured tasks like programming guidance, math problem-solving, and logic-based instructions.

Run offline on local machines with quantized models, no cloud API required.

Perfect for academic labs exploring SFT, instruction tuning, or evaluating open LLM alignment workflows.

OpenAssistant‑SFT‑7

vs

30B‑Scale Models

Feature	Vicuna‑33B	OpenAssistant‑SFT‑7‑30B	GPT4All‑13B
Base Model	LLaMA‑33B	LLaMA‑30B	LLaMA / Falcon 13B
Instruction Data	ShareGPT dialogs	Diverse OASST+datasets	Mixed open corpora
SFT Epoch	N/A (baseline dialog)	Epoch 7 supervised fine-tune	Mixed tuning sources
Quantization Options	Available	XOR + GPTQ quant formats	GGUF quant variants
Inference Efficiency	Moderate to heavy	Moderate (17–20 GB)	High (8–10 GB)
Licensing	Research-only (LLaMA)	Research-only (LLaMA)	Non-commercial/local use