Bielik 1.5B V3.0 Instruct FP8 Dynamic

🚀 Bielik-1.5B-v3.0-Instruct-FP8-Dynamic

This model is obtained by quantizing the weights and activations of Bielik-1.5B-v3.0-Instruct to FP8 data type, enabling efficient inference with vLLM >= 0.5.0 or SGLang.

🚀 Quick Start

This model was obtained by quantizing the weights and activations of Bielik-1.5B-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.

🔗 Technical report: https://arxiv.org/abs/2505.02550

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

⚠️ Important Note

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

💻 Usage Examples

Basic Usage

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-1.5B-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": "Jeste≈õ 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.

Advanced Usage

Use with SGLang Runtime

Launch a server of SGLang Runtime:

python -m sglang.launch_server --model-path speakleash/Bielik-1.5B-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": "Jeste≈õ pomocnym asystentem Bielik."},
        {"role": "user", "content": "Kim był Mikołaj Kopernik i z czego zasłynął?"},
    ],
    temperature=0,
    max_tokens=4096,
)
print(response)

📚 Documentation

Model description:

Responsible for model quantization

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

📄 License

This model is under the Apache 2.0 license and Terms of Use.

📞 Contact Us

If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our Discord SpeakLeash.