Instructions to use szwagros/Lens-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use szwagros/Lens-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("szwagros/Lens-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - llama-cpp-python
How to use szwagros/Lens-Turbo with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="szwagros/Lens-Turbo", filename="text_encoder/gpt-oss-20b-UD-Q4_K_XL.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use szwagros/Lens-Turbo with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf szwagros/Lens-Turbo:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf szwagros/Lens-Turbo:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf szwagros/Lens-Turbo:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf szwagros/Lens-Turbo:UD-Q4_K_XL
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 szwagros/Lens-Turbo:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf szwagros/Lens-Turbo:UD-Q4_K_XL
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 szwagros/Lens-Turbo:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf szwagros/Lens-Turbo:UD-Q4_K_XL
Use Docker
docker model run hf.co/szwagros/Lens-Turbo:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use szwagros/Lens-Turbo with Ollama:
ollama run hf.co/szwagros/Lens-Turbo:UD-Q4_K_XL
- Unsloth Studio new
How to use szwagros/Lens-Turbo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for szwagros/Lens-Turbo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for szwagros/Lens-Turbo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for szwagros/Lens-Turbo to start chatting
- Pi new
How to use szwagros/Lens-Turbo with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf szwagros/Lens-Turbo:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "szwagros/Lens-Turbo:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use szwagros/Lens-Turbo with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf szwagros/Lens-Turbo:UD-Q4_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default szwagros/Lens-Turbo:UD-Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use szwagros/Lens-Turbo with Docker Model Runner:
docker model run hf.co/szwagros/Lens-Turbo:UD-Q4_K_XL
- Lemonade
How to use szwagros/Lens-Turbo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull szwagros/Lens-Turbo:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Lens-Turbo-UD-Q4_K_XL
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "GptOssForCausalLM" | |
| ], | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": null, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 200002, | |
| "experts_per_token": 4, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 2880, | |
| "initial_context_length": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2880, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 131072, | |
| "model_type": "gpt_oss", | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 4, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 8, | |
| "num_local_experts": 32, | |
| "output_router_logits": false, | |
| "pad_token_id": 199999, | |
| "quantization_config": { | |
| "modules_to_not_convert": [ | |
| "model.layers.*.self_attn", | |
| "model.layers.*.mlp.router", | |
| "model.embed_tokens", | |
| "lm_head" | |
| ], | |
| "quant_method": "mxfp4" | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 32.0, | |
| "original_max_position_embeddings": 4096, | |
| "rope_theta": 150000, | |
| "rope_type": "yarn", | |
| "truncate": false | |
| }, | |
| "router_aux_loss_coef": 0.9, | |
| "sliding_window": 128, | |
| "swiglu_limit": 7.0, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.8.0", | |
| "use_cache": true, | |
| "vocab_size": 201088 | |
| } | |