Bielik-11B-v3.0-Instruct
Bielik-11B-v3.0-Instruct is an 11B-parameter instruction-tuned LLM optimized for multilingual conversational tasks, supporting 20+ languages including Polish, English, and European languages. It is gated on HuggingFace and licensed under Apache 2.0, making it suitable for private deployment and custom application development.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | speakleash |
| Parameters | 11.2B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | Yes |
| Downloads | 432.1k |
| Likes | 68 |
| Last updated | 2026-07-01 |
| Source | speakleash/Bielik-11B-v3.0-Instruct |
What Bielik-11B-v3.0-Instruct is
An 11.2B-parameter transformer-based model distributed via HuggingFace in safetensors format. Pipeline: text-generation (conversational). Gated model requiring access approval. No context length, architecture details, or training data composition documented in provided data. Supports transformer-compatible serving frameworks.
Run Bielik-11B-v3.0-Instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="speakleash/Bielik-11B-v3.0-Instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE for inference (unverified): 11B model in FP16 ≈ 22 GB VRAM per GPU; quantized (int8/int4) ≈ 6–12 GB. Fine-tuning with LoRA: ≈ 16 GB VRAM. Verify with profiling in your target environment; actual requirement depends on batch size, sequence length, and serving framework optimization.
Model architecture is LLaMA-compatible (per tags), making LoRA and QLoRA fine-tuning feasible via standard tooling (e.g., PEFT, Unsloth). No documented fine-tuning examples or baseline accuracy/loss curves provided. Recommend benchmarking on a small validation set before full training runs.
When to avoid it — and what to weigh
- Real-time latency-critical applications — 11B model size and unknown context length make ultra-low-latency inference challenging; benchmark serving performance in your target environment first.
- Single-language, non-European language requirements — Model is optimized for European language families; if your primary use case is languages outside its trained set, consider single-language or more comprehensive multilingual alternatives.
- Requiring continuous automated updates — Last modified 2026-07-01; no documented release cadence or auto-update strategy. Plan manual version management and testing cycles.
- Semantic tasks requiring very long context — Context length is unknown; if your use case requires >4k-token windows, verify capability before committing resources.
License & commercial use
Apache 2.0 license (OSI-approved permissive license). Permits commercial use, modification, and redistribution with attribution and liability disclaimer.
Apache 2.0 permits commercial use. However, the model is gated on HuggingFace, requiring manual access approval from speakleash. Terms of gate approval are unknown and may impose additional restrictions. Before commercial deployment, confirm with speakleash that gate approval covers your intended use case and scale. No commercial support or SLA documented.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Limited |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Gated model reduces unauthorized distribution risk. No security audit, adversarial robustness testing, or jailbreak evaluation documented. When deployed, apply standard LLM safety practices: input validation, output filtering, rate limiting, and monitoring for prompt injection. Multilingual support increases surface area for encoding-based attacks—test with non-Latin scripts in your threat model.
Alternatives to consider
Mistral 7B Instruct
Smaller (7B), ungated, strong instruction-following, better-documented. Suitable if European multilingual support is not critical and you prefer easier deployment.
Llama 2 13B Chat
13B, well-established, Meta-supported, extensive community tooling. Trade-off: less specialized for European languages but more mature ecosystem.
Qwen 14B Instruct
14B, multilingual (40+ languages), strong benchmarks, active maintenance. Better documented than Bielik; good if your language set spans beyond European.
Ship Bielik-11B-v3.0-Instruct with senior software developers
Request gated access on HuggingFace, profile memory requirements in your environment, and review the full model card for context length and training details before integrating. Consider Devco's private LLM and RAG services to accelerate safe, compliant deployment.
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Bielik-11B-v3.0-Instruct FAQ
Can I use Bielik-11B commercially?
What is the context length?
How much GPU memory do I need to run this?
Is the model actively maintained?
Custom software development services
Need help beyond evaluating Bielik-11B-v3.0-Instruct? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Bielik-11B?
Request gated access on HuggingFace, profile memory requirements in your environment, and review the full model card for context length and training details before integrating. Consider Devco's private LLM and RAG services to accelerate safe, compliant deployment.