Mistral 7B Instruct v0.2
Mistral 7B Instruct v0.2 is a 7-billion-parameter instruction-tuned model from Mistral AI with a 32,768-token context window. It uses `[INST]` prompt tags and is distributed here as TheBloke's GGUF quantizations for CPU and GPU inference. Apache 2.0 licensed, so commercial use is allowed without restrictions.
For a 7B model, the 32K context is the headline feature and it's genuine — useful if you need to process longer documents without chunking. It's a reliable, well-tested base for local inference and the Apache 2.0 license removes any commercial friction. That said, if raw capability is your priority, newer Mistral releases or larger models will outperform it. Recommend if context length or licensing matters to you; otherwise check what's newer first.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.05/10. License is explicitly apache-2.0 on the card and correctly flagged commercial-OK. Metadata (7B, 32K context, Mistral family, GGUF quants) matches the card and known Mistral v0.2 specs. Editorial voice is honest — notes v0.2 is not the latest, calls out quant tradeoffs, and doesn't oversell. The 'german' useCase tag is odd and unsupported by the card (minor flag), and bestUseCase is somewhat generic ('document Q&A with long context') but acceptable given the model's broad instruct nature. Deployability is well-covered (llama.cpp requirement, quant tradeoffs). Passes the bar but the stray 'german' tag should be reviewed.
Flags: - useCases includes 'german' with no supporting evidence in the HF card — likely a tagging artifact, should be removed - bestUseCase phrasing is moderately generic; could be sharpened
Overview
Mistral 7B Instruct v0.2 is a 7-billion-parameter instruction-tuned model from Mistral AI with a 32,768-token context window. It uses `[INST]` prompt tags and is distributed here as TheBloke's GGUF quantizations for CPU and GPU inference. Apache 2.0 licensed, so commercial use is allowed without restrictions.
Strengths
- 32,768-token context — unusually long for a 7B model
- Apache 2.0 license: commercial use permitted
- Multiple GGUF quant levels available — tune for your VRAM/quality tradeoff
- 139k+ HF downloads suggests broad real-world testing
Weaknesses
- Smaller quants (Q4 and below) will degrade output quality vs. full precision
- Requires llama.cpp-compatible runtime — not plug-and-play for all setups
- 7B parameter ceiling means it will struggle on complex reasoning or multi-step tasks
- v0.2 is not the latest Mistral release; newer variants exist
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 Instruct v0.2.
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 Instruct v0.2?
Can I use Mistral 7B Instruct v0.2 commercially?
What's the context length of Mistral 7B Instruct v0.2?
Source: huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Mistral 7B Instruct v0.2 runs on your specific hardware before committing money.