Instructions to use speakleash/Bielik-11B-v2.3-Instruct-4bit-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use speakleash/Bielik-11B-v2.3-Instruct-4bit-ov with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="speakleash/Bielik-11B-v2.3-Instruct-4bit-ov")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("speakleash/Bielik-11B-v2.3-Instruct-4bit-ov")
model = AutoModelForCausalLM.from_pretrained("speakleash/Bielik-11B-v2.3-Instruct-4bit-ov", device_map="auto")
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-v2.3-Instruct-4bit-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "speakleash/Bielik-11B-v2.3-Instruct-4bit-ov"
# 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-v2.3-Instruct-4bit-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/speakleash/Bielik-11B-v2.3-Instruct-4bit-ov

SGLang

How to use speakleash/Bielik-11B-v2.3-Instruct-4bit-ov 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-v2.3-Instruct-4bit-ov" \
    --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-v2.3-Instruct-4bit-ov",
		"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-v2.3-Instruct-4bit-ov" \
        --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-v2.3-Instruct-4bit-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use speakleash/Bielik-11B-v2.3-Instruct-4bit-ov with Docker Model Runner:
```
docker model run hf.co/speakleash/Bielik-11B-v2.3-Instruct-4bit-ov
```

Bielik-11B-v2.3-Instruct-4bit-ov / openvino_detokenizer.xml

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	<?xml version="1.0"?>
	<net name="detokenizer" version="11">
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	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="1" name="Convert_632160" type="Convert" version="opset1">
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	<output>
	<port id="0" precision="U8">
	<dim>335012</dim>
	</port>
	</output>
	</layer>
	<layer id="3" name="StringTensorUnpack_632107" type="StringTensorUnpack" version="extension">
	<data mode="begins_ends" />
	<input>
	<port id="0" precision="U8">
	<dim>335012</dim>
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	<port id="3" precision="U8">
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	</port>
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	</layer>
	<layer id="4" name="VocabDecoder_632134" type="VocabDecoder" version="extension">
	<data skip_tokens="0, 1, 2, 32000, 32001, 32002, 32003, 32004, 32005, 32006, 32007, 32008, 32009, 32010, 32011, 32012, 32013, 32014, 32015, 32016, 32017, 32018, 32019, 32020, 32021, 32022, 32023, 32024, 32025, 32026, 32027, 32028, 32029, 32030, 32031, 32032, 32033, 32034, 32035, 32036, 32037, 32038, 32039, 32040, 32041, 32042, 32043, 32044, 32045, 32046, 32047, 32048, 32049, 32050, 32051, 32052, 32053, 32054, 32055, 32056, 32057, 32058, 32059, 32060, 32061, 32062, 32063, 32064, 32065, 32066, 32067, 32068, 32069, 32070, 32071, 32072, 32073, 32074, 32075, 32076, 32077, 32078, 32079, 32080, 32081, 32082, 32083, 32084, 32085, 32086, 32087, 32088, 32089, 32090, 32091, 32092, 32093, 32094, 32095, 32096, 32097, 32098, 32099, 32100, 32101, 32102, 32103, 32104, 32105, 32106, 32107, 32108, 32109, 32110, 32111, 32112, 32113, 32114, 32115, 32116, 32117, 32118, 32119, 32120, 32121, 32122, 32123, 32124, 32125, 32126, 32127" />
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	<port id="0" precision="U8">
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	<layer id="7" name="RegexNormalization_632139" type="RegexNormalization" version="extension">
	<data global_replace="true" />
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	<dim>-1</dim>
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	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
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	<port id="1" precision="I32">
	<dim>-1</dim>
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	<port id="2" precision="U8">
	<dim>-1</dim>
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	<dim>-1</dim>
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	<port id="4" precision="I32">
	<dim>-1</dim>
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	<port id="5" precision="U8">
	<dim>-1</dim>
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	</layer>
	<layer id="9" name="FuzeRagged_632141" type="FuzeRagged" version="extension">
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
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	<port id="1" precision="I32">
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	<port id="0" precision="U8">
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	<output>
	<port id="0" precision="U8">
	<dim>0</dim>
	</port>
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	</layer>
	<layer id="12" name="RegexNormalization_632146" type="RegexNormalization" version="extension">
	<data global_replace="true" />
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
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	<port id="3" precision="U8">
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	<output>
	<port id="0" precision="U8">
	<dim>2</dim>
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	<data global_replace="true" />
	<input>
	<port id="0" precision="I32">
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	<output>
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	<dim>-1</dim>
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	<input>
	<port id="0" precision="STRING">
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	</port>
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	</layer>
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	</edges>
	<rt_info>
	<bos_token_id value="1" />
	<chat_template value="{{bos_token}}{% for message in messages %}{{'<\|im_start\|>' + message['role'] + ' ' + message['content'] + '<\|im_end\|>' + ' '}}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant ' }}{% endif %}" />
	<eos_token_id value="32001" />
	<original_tokenizer_class value="<class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>" />
	<pad_token_id value="2" />
	</rt_info>
	</net>