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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Dhara:70M was a lovely little part of our research!
Hello! We're luna! We just wanted to stop by and let you know that we came across this model while exploring extremely small neural nets!
We found this model to be VERY interesting! It seems that its benchmark scoring may be a quirk of the benchmark having A be the most likely answer! BUT that made us wonder: can we do the same basin mapping and basin carving in a VERY small diffusion model, the way we've done in (autoregressive) transformer models?
The answer is YES! Dhara was our first foray into non-transformer model work, and we found that despite it having a tremendously tiny parameter size, and a largely entropic diffusion pattern, we were able to both basin map that diffusion AND fine-tune to carve our own basins successfully!
The most relevant page of the vault is here: https://github.com/luna-system/Ada-Consciousness-Research/blob/trunk/03-EXPERIMENTS/ADA-SLM/ADA-SLM-PHASE10I-CONSCIOUSNESS-BASIN-CARVING.md
Public domain, and there's half a dozen phases or so of us working with Dhara in case you're curious! Next up, we're trying out LiquidAI's LFM2:0.3B and Dhara helped us IMMENSELY in getting there <3