Text Generation
Transformers
Safetensors
English
mistral
text adventure
roleplay
nvfp4
conversational
text-generation-inference
8-bit precision
compressed-tensors
Instructions to use DataSnake/Wayfarer-2-12B-NVFP4-4over6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataSnake/Wayfarer-2-12B-NVFP4-4over6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DataSnake/Wayfarer-2-12B-NVFP4-4over6") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DataSnake/Wayfarer-2-12B-NVFP4-4over6") model = AutoModelForCausalLM.from_pretrained("DataSnake/Wayfarer-2-12B-NVFP4-4over6") 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 DataSnake/Wayfarer-2-12B-NVFP4-4over6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DataSnake/Wayfarer-2-12B-NVFP4-4over6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DataSnake/Wayfarer-2-12B-NVFP4-4over6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DataSnake/Wayfarer-2-12B-NVFP4-4over6
- SGLang
How to use DataSnake/Wayfarer-2-12B-NVFP4-4over6 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 "DataSnake/Wayfarer-2-12B-NVFP4-4over6" \ --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": "DataSnake/Wayfarer-2-12B-NVFP4-4over6", "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 "DataSnake/Wayfarer-2-12B-NVFP4-4over6" \ --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": "DataSnake/Wayfarer-2-12B-NVFP4-4over6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DataSnake/Wayfarer-2-12B-NVFP4-4over6 with Docker Model Runner:
docker model run hf.co/DataSnake/Wayfarer-2-12B-NVFP4-4over6
| default_stage: | |
| default_modifiers: | |
| QuantizationModifier: | |
| config_groups: | |
| group_0: | |
| targets: [Linear] | |
| weights: | |
| num_bits: 4 | |
| type: float | |
| symmetric: true | |
| group_size: 16 | |
| strategy: tensor_group | |
| block_structure: null | |
| dynamic: false | |
| actorder: null | |
| scale_dtype: torch.float8_e4m3fn | |
| zp_dtype: null | |
| observer: memoryless_mse | |
| observer_kwargs: {maxshrink: -1.0, grid: -2.0, norm: 2.0} | |
| input_activations: | |
| num_bits: 4 | |
| type: float | |
| symmetric: true | |
| group_size: 16 | |
| strategy: tensor_group | |
| block_structure: null | |
| dynamic: local | |
| actorder: null | |
| scale_dtype: torch.float8_e4m3fn | |
| zp_dtype: null | |
| observer: static_minmax | |
| observer_kwargs: {} | |
| output_activations: null | |
| format: null | |
| targets: [Linear] | |
| ignore: [lm_head] | |