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---
language:
- pl
license: apache-2.0
library_name: transformers
tags:
- finetuned
- gguf
- 8bit
inference: false
pipeline_tag: text-generation
base_model: speakleash/Bielik-11B-v2.0-Instruct
---
<p align="center">
<img src="https://huggingface.co./speakleash/Bielik-7B-Instruct-v0.1-GGUF/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.2-Instruct-FP8
This model was obtained by quantizing the weights and activations of [Bielik-11B-v.2.0-Instruct](https://huggingface.co./speakleash/Bielik-11B-v2.0-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](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "speakleash/Bielik-11B-v2.0-Instruct-FP8"
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](https://docs.vllm.ai/en/latest/) for more details.
## Use with SGLang Runtime
Launch a server of SGLang Runtime:
```
python -m sglang.launch_server --model-path speakleash/Bielik-11B-v2.0-Instruct-FP8 --port 30000
```
Then you can send http request or use OpenAI Compatible API.
```python
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](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Quant from:** [Bielik-11B-v2.0-Instruct](https://huggingface.co./speakleash/Bielik-11B-v2.0-Instruct)
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co./speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
### Responsible for model quantization
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.
## 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](https://discord.gg/CPBxPce4). |