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 / README.md

SamoXXX

Update README.md

fad07e5 verified almost 2 years ago

preview code

Raw

History Blame Contribute Delete

4.7 kB

	---
	language:
	- pl
	license: apache-2.0
	library_name: transformers
	tags:
	- finetuned
	- gguf
	inference: false
	pipeline_tag: text-generation
	base_model: speakleash/Bielik-11B-v2.3-Instruct
	---

	<p align="center">
	<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
	</p>

	# Bielik-11B-v2.3-Instruct-GPTQ

	This repo contains OpenVino 4bit format model files for [SpeakLeash](https://speakleash.org/)'s [Bielik-11B-v.2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct).

	<b><u>DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!</u></b><br>
	### Model usage with OpenVino
	This model can be deployed efficiently using the [OpenVino](https://docs.openvino.ai/2024/index.html). Below you can find two ways of model inference: using Intel Optimum, pure OpenVino library.

	The most simple LLM inferencing code with OpenVINO and the optimum-intel library.
	```python
	from optimum.intel import OVModelForCausalLM
	from transformers import AutoTokenizer

	model_id = "speakleash/Bielik-11B-v2.3-Instruct-4bit-ov"
	model = OVModelForCausalLM.from_pretrained(model_id, use_cache=False)

	question = "Dlaczego ryby nie potrafią fruwać?"

	prompt_text_bielik = f"""<s><\|im_start\|> system
	Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<\|im_end\|>
	<\|im_start\|> user
	{question}<\|im_end\|>
	<\|im_start\|> assistant
	"""

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	inputs = tokenizer(prompt_text_bielik, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=500)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	Run an LLM model with only OpenVINO (additionaly we provided code which uses 'greedy decoding' instead of sampling).
	```python
	import openvino as ov
	import numpy as np
	from transformers import AutoTokenizer

	model_path = "speakleash/Bielik-11B-v2.3-Instruct-4bit-ov/openvino_model.xml"
	tokenizer = AutoTokenizer.from_pretrained("speakleash/Bielik-11B-v2.3-Instruct-4bit-ov")

	ov_model = ov.Core().read_model(model_path)
	compiled_model = ov.compile_model(ov_model, "CPU")
	infer_request = compiled_model.create_infer_request()

	question = "Dlaczego ryby nie potrafią fruwać?"
	prompt_text_bielik = f"""<s><\|im_start\|> system
	Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim.<\|im_end\|>
	<\|im_start\|> user
	{question}<\|im_end\|>
	<\|im_start\|> assistant
	"""

	tokens = tokenizer.encode(prompt_text_bielik, return_tensors="np")
	input_ids = tokens
	attention_mask = np.ones_like(input_ids)
	position_ids = np.arange(len(tokens[0])).reshape(1, -1)
	beam_idx = np.array([0], dtype=np.int32)

	infer_request.reset_state()

	prev_output = ''
	generated_text_ids = np.array([], dtype=np.int32)
	num_max_token_for_generation = 500

	print(f'Pytanie: {question}')
	print("Odpowiedź:", end=' ', flush=True)

	for _ in range(num_max_token_for_generation):
	response = infer_request.infer(inputs={
	'input_ids': input_ids,
	'attention_mask': attention_mask,
	'position_ids': position_ids,
	'beam_idx': beam_idx
	})

	next_token_logits = response['logits'][0, -1, :]
	sampled_id = np.argmax(next_token_logits) # Greedy decoding
	generated_text_ids = np.append(generated_text_ids, sampled_id)

	output_text = tokenizer.decode(generated_text_ids)
	print(output_text[len(prev_output):], end='', flush=True)
	prev_output = output_text

	input_ids = np.array([[sampled_id]], dtype=np.int64)
	attention_mask = np.array([[1]], dtype=np.int64)
	position_ids = np.array([[position_ids[0, -1] + 1]], dtype=np.int64)

	if sampled_id == tokenizer.eos_token_id:
	print('\n\n*** Zakończono generowanie.')
	break

	print(f'\n\n*** Wygenerowano {len(generated_text_ids)} tokenów.')
	```

	### 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-v2.3-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-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).