Mistral 7B OpenOrca GGUF
Mistral 7B fine-tuned on the OpenOrca instruction dataset, distributed by TheBloke in GGUF format for local CPU and GPU inference. Uses ChatML prompt formatting and supports up to 32,768 tokens of context. Apache-2.0 licensed, so commercial use is permitted.
A solid, no-cost option if you need a capable 7B instruction model that actually runs on modest hardware. The 32K context is a genuine practical advantage at this parameter count. That said, if your workload is German-language, look elsewhere — this model was not built for it and there is no evidence it handles it well. Hedge: worth a quick benchmark on your specific task before committing.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.15/10. License is explicit Apache-2.0 in the HF card and correctly flagged commercial-OK. Vendor (TheBloke as quantizer), family (mistral), 7B params, and 32K context all match metadata. The description is honest and operator-voiced, with a strong hedge against the misleading 'german' useCase tag — though that tag should probably be removed rather than rebutted in the weaknesses. bestUseCase is reasonably specific (CPU/low-VRAM English instruction-following) and the strengths/weaknesses are concrete. Minor concern: the useCases array includes 'german' which the row itself disclaims — this is an inconsistency the row papers over rather than fixes.
Flags: - useCases includes 'german' but the row explicitly warns German performance is unreliable — drop the tag instead of contradicting it - Mistral 7B v0.1 base actually has a 8K sliding-window attention; the 32K figure comes from config but real-world long-context quality at 32K is weaker than implied
Overview
Mistral 7B fine-tuned on the OpenOrca instruction dataset, distributed by TheBloke in GGUF format for local CPU and GPU inference. Uses ChatML prompt formatting and supports up to 32,768 tokens of context. Apache-2.0 licensed, so commercial use is permitted.
Strengths
- 32,768-token context window — large for a 7B model
- GGUF quantization makes it runnable on consumer hardware without a full GPU
- Apache-2.0 license: free for commercial use
- OpenOrca fine-tune improves general instruction-following over base Mistral 7B
Weaknesses
- Quantized weights mean some quality degradation versus the FP16 original — degree varies by quant level chosen
- Requires a GGUF-compatible runtime (llama.cpp, LM Studio, etc.) — not a drop-in for standard HuggingFace pipelines
- Primarily English training data; German-language performance is unreliable
- 9K downloads and 242 likes suggest limited community validation relative to larger TheBloke releases
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 3.9 GB | 5 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Mistral 7B OpenOrca GGUF.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Mistral 7B OpenOrca GGUF?
Can I use Mistral 7B OpenOrca GGUF commercially?
What's the context length of Mistral 7B OpenOrca GGUF?
Source: huggingface.co/TheBloke/Mistral-7B-OpenOrca-GGUF
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Mistral 7B OpenOrca GGUF runs on your specific hardware before committing money.