Text Generation
Transformers
Safetensors
modilify_mk2
feature-extraction
diffusion
mixture-of-experts
custom-code
modilify-mk2
conversational
custom_code
Instructions to use modilify/Modilify-Mk2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modilify/Modilify-Mk2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="modilify/Modilify-Mk2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modilify/Modilify-Mk2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use modilify/Modilify-Mk2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "modilify/Modilify-Mk2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modilify/Modilify-Mk2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/modilify/Modilify-Mk2
- SGLang
How to use modilify/Modilify-Mk2 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 "modilify/Modilify-Mk2" \ --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": "modilify/Modilify-Mk2", "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 "modilify/Modilify-Mk2" \ --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": "modilify/Modilify-Mk2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use modilify/Modilify-Mk2 with Docker Model Runner:
docker model run hf.co/modilify/Modilify-Mk2
Download generation_config.json from modilify/Modilify-Mk2: direct link, hf CLI and curl.
- Browser
- Download file 752 Bytes
-
https://huggingface.co/modilify/Modilify-Mk2/resolve/main/generation_config.json
- Command line
-
hf download hf://modilify/Modilify-Mk2/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/modilify/Modilify-Mk2/resolve/main/generation_config.json
752 Bytes
| { | |
| "bos_token_id": 2, | |
| "confidence_threshold": null, | |
| "eos_token_id": [ | |
| 1, | |
| 50 | |
| ], | |
| "jump_on_no_progress_after": 12, | |
| "max_denoising_steps": 256, | |
| "max_new_tokens": 256, | |
| "max_ponder_steps": 64, | |
| "min_trajectory_progress": 0.005, | |
| "pad_token_id": 0, | |
| "repetition_penalty": 1.0, | |
| "repetition_penalty_exclude_token_ids": [ | |
| 2, | |
| 1, | |
| 3, | |
| 0, | |
| 4, | |
| 258881, | |
| 256000, | |
| 255999, | |
| 258883, | |
| 101, | |
| 258882, | |
| 106, | |
| 52, | |
| 49, | |
| 47, | |
| 51, | |
| 258880, | |
| 100, | |
| 105, | |
| 48, | |
| 46, | |
| 50, | |
| 98, | |
| 258884 | |
| ], | |
| "return_dict_in_generate": true, | |
| "sampler_config": null, | |
| "stability_threshold": null, | |
| "t_max": 0.8, | |
| "t_min": 0.8, | |
| "transformers_version": "5.14.1", | |
| "turn_end_token_id": 106 | |
| } | |