Create README.md

README.md

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+---
+language:
+- pl
+license: apache-2.0
+library_name: transformers
+tags:
+- finetuned
+- gguf
+- 8bit
+inference: false
+pipeline_tag: text-generation
+---
+<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-W8A8
+
+This model was obtained by quantizing the weights and activations of [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct) to W8A8 (int8) data type, ready for inference with vLLM >= 0.5.0. 
+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).
+
+
+## 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-v2.2-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-v2.2-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-v2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct)
+* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
+* **License:** apache-2.0 
+
+### 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).