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)# 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
File size: 1,031 Bytes
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"architectures": [
"DharaForMaskedDiffusion"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "modeling_dhara.DharaConfig",
"AutoModel": "modeling_dhara.DharaForMaskedDiffusion",
"AutoModelForCausalLM": "modeling_dhara.DharaForMaskedDiffusion"
},
"bos_token_id": 1,
"canon_activation": false,
"canon_bias": false,
"canon_kernel": 4,
"canon_residual": true,
"canon_set": "AC",
"eos_token_id": 2,
"head_dim": 48,
"hidden_act": "silu",
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1024,
"mask_epsilon": 0.001,
"mask_token_id": 50256,
"max_position_embeddings": 1024,
"model_type": "dhara",
"num_attention_heads": 8,
"num_diffusion_steps": 1000,
"num_hidden_layers": 32,
"num_key_value_heads": 4,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"torch_dtype": "bfloat16",
"transformers_version": "4.55.2",
"use_cache": false,
"use_flash_attention": false,
"use_xformers": false,
"vocab_size": 50304
}
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