Text Generation
Transformers
Safetensors
English
dhara
feature-extraction
diffusion
language-model
causal-lm
custom_code
Eval Results (legacy)
Instructions to use codelion/dhara-70m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codelion/dhara-70m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codelion/dhara-70m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codelion/dhara-70m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codelion/dhara-70m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codelion/dhara-70m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codelion/dhara-70m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codelion/dhara-70m
- SGLang
How to use codelion/dhara-70m 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 "codelion/dhara-70m" \ --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": "codelion/dhara-70m", "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 "codelion/dhara-70m" \ --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": "codelion/dhara-70m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codelion/dhara-70m with Docker Model Runner:
docker model run hf.co/codelion/dhara-70m
Commit History
-
Adding `transformers` as the library name
Verified signature08c8e9b
-
Upload README.md with huggingface_hub
Verified signature823d575 -
Upload README.md with huggingface_hub
Verified signature531cfb6 -
Upload README.md with huggingface_hub
Verified signature95af59a -
Upload README.md with huggingface_hub
Verified signatured20925d -
Upload README.md with huggingface_hub
Verified signaturef525386 -
Upload README.md with huggingface_hub
Verified signaturef58fe7f -
Upload README.md with huggingface_hub
Verified signature830aaca -
Upload README.md with huggingface_hub
Verified signaturefbf5200 -
Upload README.md with huggingface_hub
Verified signature4f52be5 -
Upload README.md with huggingface_hub
Verified signature160d6c3 -
Upload model.safetensors with huggingface_hub
Verified signaturec57ecf4 -
Upload folder using huggingface_hub
Verified signature575face -
Update code examples with diffusion-specific parameters
Verified signature40643a7 -
Add diffusion-based generate() method for proper text generation
Verified signature671befc
-
Update model card
Verified signature1c747f7 -
Upload folder using huggingface_hub
Verified signature56202a2 -
Initial upload of Dhara-70M diffusion language model
Verified signature7d2bb1c -
initial commit
Verified signature7f03436