language, license, library_name, tags, inference, pipeline_tag, base_model

language	license	library_name	tags	inference	pipeline_tag	base_model
multilingual pl en sq bel bs bg hr cs da et fi fr el es is lt nl de no pt ru ro sr hbs sv sk sl tr uk hu it lv	apache-2.0	transformers	finetuned gguf 8bit	false	text-generation	speakleash/Bielik-11B-v3.0-Instruct

Bielik-11B-v3.0-Instruct-FP8-Dynamic

This model was obtained by quantizing the weights and activations of Bielik-11B-v3.0-Instruct to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang. 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%. 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.

FP8 compuation is supported on Nvidia GPUs with compute capability > 8.9 (Ada Lovelace, Hopper).

DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "speakleash/Bielik-11B-v3.0-Instruct-FP8-Dynamic"

sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=4096)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
    {"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
]

prompts = tokenizer.apply_chat_template(messages, tokenize=False)

llm = LLM(model=model_id, max_model_len=4096)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Use with SGLang Runtime

Launch a server of SGLang Runtime:

python -m sglang.launch_server --model-path speakleash/Bielik-11B-v3.0-Instruct-FP8-Dynamic --port 30000

Then you can send http request or use OpenAI Compatible API.

import openai
client = openai.Client(
    base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "system", "content": "Jesteś pomocnym asystentem Bielik."},
        {"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
    ],
    temperature=0,
    max_tokens=4096,
)
print(response)

Model description:

• Developed by: SpeakLeash & ACK Cyfronet AGH
• Language: Multilingual (32 European languages, optimized for Polish)
• Model type: causal decoder-only
• Quant from: Bielik-11B-v3.0-Instruct
• Finetuned from: speakleash/Bielik-11B-v3-Base-20250730
• License: Apache 2.0

Responsible for model quantization

• Remigiusz Kinas^SpeakLeash - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.

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