Text Generation
Transformers
Safetensors
Polish
llama
finetuned
bnb
conversational
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit") model = AutoModelForCausalLM.from_pretrained("speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit
- SGLang
How to use speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit with Docker Model Runner:
docker model run hf.co/speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Quick Links
Bielik-11B-v2.6-Instruct-bnb-4bit
This repo contains Bitsandbytes format model files for SpeakLeash's Bielik-11B-v2.6-Instruct.
DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!
Model description:
- Developed by: SpeakLeash & ACK Cyfronet AGH
- Language: Polish
- Model type: causal decoder-only
- Quant from: Bielik-11B-v2.6-Instruct
- Finetuned from: Bielik-11B-v2
- License: Apache 2.0 and Terms of Use
Responsible for model quantization
- Remigiusz KinasSpeakLeash - 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.
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Model tree for speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit
Base model
speakleash/Bielik-11B-v2 Finetuned
speakleash/Bielik-11B-v2.6-Instruct
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "speakleash/Bielik-11B-v2.6-Instruct-bnb-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'