Bielik-11B-v2.3-Instruct-4bit-ov

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

This repo contains OpenVino 4bit format model files for SpeakLeash's Bielik-11B-v.2.3-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. 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.

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).

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 & ACK Cyfronet AGH
• Language: Polish
• Model type: causal decoder-only
• Quant from: Bielik-11B-v2.3-Instruct
• Finetuned from: Bielik-11B-v2
• License: Apache 2.0 and Terms of Use

Responsible for model quantization

• Remigiusz Kinas^SpeakLeash - team leadership, conceptualizing, calibration data preparation, process creation and quantized model delivery.

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