Text Generation
Transformers
Safetensors
English
metadiffusion
diffusion
diffusion-lm
ar-to-diffusion
custom_code
Instructions to use CodeSoft/MetaDiffusion-600M-ChatBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeSoft/MetaDiffusion-600M-ChatBase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CodeSoft/MetaDiffusion-600M-ChatBase with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CodeSoft/MetaDiffusion-600M-ChatBase" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CodeSoft/MetaDiffusion-600M-ChatBase", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase
- SGLang
How to use CodeSoft/MetaDiffusion-600M-ChatBase 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 "CodeSoft/MetaDiffusion-600M-ChatBase" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CodeSoft/MetaDiffusion-600M-ChatBase", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "CodeSoft/MetaDiffusion-600M-ChatBase" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CodeSoft/MetaDiffusion-600M-ChatBase", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CodeSoft/MetaDiffusion-600M-ChatBase with Docker Model Runner:
docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "chat_template": "{% for message in messages %}{{ '<|im_start|>' + message['role'] }}\n{{ message['content'] }}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "[MASK]", | |
| "<|r1|>", | |
| "<|r2|>", | |
| "<|r3|>", | |
| "<|r4|>", | |
| "<|r5|>", | |
| "<|r6|>", | |
| "<|r7|>" | |
| ], | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } |