Instructions to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="second-state/Bielik-4.5B-v3.0-Instruct-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("second-state/Bielik-4.5B-v3.0-Instruct-GGUF") model = AutoModelForCausalLM.from_pretrained("second-state/Bielik-4.5B-v3.0-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "second-state/Bielik-4.5B-v3.0-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "second-state/Bielik-4.5B-v3.0-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
- SGLang
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF 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 "second-state/Bielik-4.5B-v3.0-Instruct-GGUF" \ --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": "second-state/Bielik-4.5B-v3.0-Instruct-GGUF", "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 "second-state/Bielik-4.5B-v3.0-Instruct-GGUF" \ --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": "second-state/Bielik-4.5B-v3.0-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Ollama:
ollama run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
- Lemonade
How to use second-state/Bielik-4.5B-v3.0-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull second-state/Bielik-4.5B-v3.0-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Bielik-4.5B-v3.0-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Update models
Browse files- .gitattributes +13 -0
- Bielik-4.5B-v3.0-Instruct-Q2_K.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-Q3_K_L.gguf +3 -0
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- Bielik-4.5B-v3.0-Instruct-Q3_K_S.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-Q4_0.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-Q4_K_M.gguf +3 -0
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- Bielik-4.5B-v3.0-Instruct-Q5_K_S.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-Q6_K.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-Q8_0.gguf +3 -0
- Bielik-4.5B-v3.0-Instruct-f16.gguf +3 -0
- config.json +30 -0
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 4,
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"pad_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 8192,
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"mlp_bias": true,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 60,
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"num_key_value_heads": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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| 29 |
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"vocab_size": 32000
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| 30 |
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}
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