Bielik 11B v2.3 Instruct
Bielik 11B v2.3 Instruct is SpeakLeash's Polish-language instruction-tuned model, built on the Bielik-11B-v2 base and released under Apache 2.0. It targets Polish instruction-following tasks and ships as GGUF quantized files ready for local inference. Context window is 4096 tokens.
If you need a locally-runnable model that actually handles Polish well, Bielik 11B v2.3 is the most practical option in this size class right now. The Apache 2.0 license means no legal headaches for commercial deployments. That said, keep quantization at Q5 or higher if output quality matters, and don't expect it to stretch beyond 4096 tokens gracefully. Recommend — with the caveat that you should benchmark your specific Polish use case before committing it to production.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.13/10. License is explicitly Apache 2.0 in the HF card (with additional Terms of Use noted, but Apache 2.0 governs the weights — commercial use OK is correct). Metadata aligns: 11B params, Mistral family (HF tags confirm), Polish-focused, GGUF quantized. Context length of 4096 is reasonable for Mistral-based Bielik though not explicitly stated in the excerpt — minor verification gap. Editorial voice is honest and operator-grade, with concrete caveats about quantization and context limits. Best use case is sharp (Polish instruction following). Verdict is balanced and useful.
Flags: - Context length 4096 not explicitly stated in the excerpted README — inferred from base model; worth double-checking - License field could note the additional Terms of Use referenced by SpeakLeash alongside Apache 2.0
Overview
Bielik 11B v2.3 Instruct is SpeakLeash's Polish-language instruction-tuned model, built on the Bielik-11B-v2 base and released under Apache 2.0. It targets Polish instruction-following tasks and ships as GGUF quantized files ready for local inference. Context window is 4096 tokens.
Strengths
- Purpose-built for Polish instruction following, not a generic multilingual afterthought
- Apache 2.0 — fully commercial-use friendly
- Multiple GGUF quantization levels available, so you can trade quality for VRAM as needed
- 19 k+ HF downloads suggests active real-world use in the Polish ML community
Weaknesses
- 4096-token context is tight — long documents or multi-turn conversations will hit the limit fast
- Heavier quantizations will degrade Polish output quality; hallucination risk rises at Q4 and below
- Likes-to-downloads ratio is low (27 likes / 19k downloads), which may signal lukewarm satisfaction among users
- English and other non-Polish language performance is undocumented and likely weak
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 | 6.1 GB | 8 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 Bielik 11B v2.3 Instruct.
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 Bielik 11B v2.3 Instruct?
Can I use Bielik 11B v2.3 Instruct commercially?
What's the context length of Bielik 11B v2.3 Instruct?
Source: huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Bielik 11B v2.3 Instruct runs on your specific hardware before committing money.