Text Generation
Transformers
Safetensors
English
dhara_ar
diffusion-llm
block-diffusion
autoregressive
self-speculation
tri-mode
conversational
custom_code
Eval Results (legacy)
Instructions to use codelion/dhara-250m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codelion/dhara-250m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codelion/dhara-250m", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("codelion/dhara-250m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codelion/dhara-250m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codelion/dhara-250m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codelion/dhara-250m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/codelion/dhara-250m
- SGLang
How to use codelion/dhara-250m 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-250m" \ --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": "codelion/dhara-250m", "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 "codelion/dhara-250m" \ --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": "codelion/dhara-250m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use codelion/dhara-250m with Docker Model Runner:
docker model run hf.co/codelion/dhara-250m
Commit History
-
Update README.md
Verified signature5f6da49
-
Revert tokenizer_config to original (transformers 5.x native format)
Verified signature71effaf -
Fix tokenizer_config for transformers 4.x compatibility (PreTrainedTokenizerFast + dict extra_special_tokens)
Verified signatured260b6c
-
Add model-index benchmark widget (diffusion mode headline scores)
Verified signatureac50c9e
-
Speed: single measured main table (batch-1 latency + peak batched, all 3 modes); drop prose caveat
Verified signaturebd77c3b -
Card: 4-col benchmarks (base/AR/dhara-diff/SmolLM) + data-efficiency framing; H100 throughput (AR 33k tok/s, honest batched caveat); sutra-10B + pedagogical citations
Verified signature497ed8c -
Add demo link to dhara-chat Space
Verified signature499767b -
Card: link NVIDIA HF blog article for the tri-mode recipe
Verified signature2fbbd00 -
Card: base_model+datasets metadata, Nemotron link, transformers-only serving, real example, context trim
Verified signature80a8276 -
Remove honest-summary blurb from model card
Verified signaturebe541d6 -
dhara-250m tri-mode (AR + block-diffusion + self-speculation), annealed, ~60B tokens
Verified signaturebd52fdd -
initial commit
Verified signature6cfd2e0