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Model: safestack/Bielik-11B-v3.0-Instruct-FP8-Dynamic Source: Original Platform
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README.md
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---
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language:
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- multilingual
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- pl
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- en
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- sq
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- bel
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- bs
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- bg
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- hr
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- cs
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- da
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- et
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- fi
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- fr
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- el
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- es
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- is
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- lt
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- nl
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- de
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- no
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- pt
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- ru
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- ro
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- sr
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- hbs
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- sv
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- sk
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- sl
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- tr
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- uk
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- hu
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- it
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- lv
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license: apache-2.0
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library_name: transformers
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tags:
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- finetuned
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- gguf
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- 8bit
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inference: false
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pipeline_tag: text-generation
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base_model: speakleash/Bielik-11B-v3.0-Instruct
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---
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<p align="center">
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<img src="https://huggingface.co/speakleash/Bielik-11B-v2/raw/main/speakleash_cyfronet.png">
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</p>
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# Bielik-11B-v3.0-Instruct-FP8-Dynamic
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This model was obtained by quantizing the weights and activations of [Bielik-11B-v3.0-Instruct](https://huggingface.co/speakleash/Bielik-11B-v3.0-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang.
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AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations.
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FP8 compuation is supported on Nvidia GPUs with compute capability > 8.9 (Ada Lovelace, Hopper).
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**DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!**
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## Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "speakleash/Bielik-11B-v3.0-Instruct-FP8-Dynamic"
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sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=4096)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
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{"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
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]
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prompts = tokenizer.apply_chat_template(messages, tokenize=False)
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llm = LLM(model=model_id, max_model_len=4096)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Use with SGLang Runtime
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Launch a server of SGLang Runtime:
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```
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python -m sglang.launch_server --model-path speakleash/Bielik-11B-v3.0-Instruct-FP8-Dynamic --port 30000
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```
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Then you can send http request or use OpenAI Compatible API.
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```python
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import openai
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client = openai.Client(
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base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
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response = client.chat.completions.create(
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model="default",
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messages=[
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{"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
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{"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
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],
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temperature=0,
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max_tokens=4096,
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)
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print(response)
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```
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### Model description:
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* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
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* **Language:** Multilingual (32 European languages, optimized for Polish)
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* **Model type:** causal decoder-only
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* **Quant from:** [Bielik-11B-v3.0-Instruct](https://huggingface.co/speakleash/Bielik-11B-v3.0-Instruct)
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* **Finetuned from:** [speakleash/Bielik-11B-v3-Base-20250730](https://huggingface.co/speakleash/Bielik-11B-v3-Base-20250730)
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* **License:** Apache 2.0
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### Responsible for model quantization
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* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
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## Contact Us
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If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/CPBxPce4).
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