Instructions to use dirac-run/ec-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dirac-run/ec-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dirac-run/ec-0.6b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dirac-run/ec-0.6b") model = AutoModelForCausalLM.from_pretrained("dirac-run/ec-0.6b", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dirac-run/ec-0.6b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dirac-run/ec-0.6b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dirac-run/ec-0.6b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dirac-run/ec-0.6b
- SGLang
How to use dirac-run/ec-0.6b 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 "dirac-run/ec-0.6b" \ --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": "dirac-run/ec-0.6b", "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 "dirac-run/ec-0.6b" \ --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": "dirac-run/ec-0.6b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dirac-run/ec-0.6b with Docker Model Runner:
docker model run hf.co/dirac-run/ec-0.6b
Download adapter/README.md from dirac-run/ec-0.6b: direct link, hf CLI and curl.
- Browser
- Download file 683 Bytes
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https://huggingface.co/dirac-run/ec-0.6b/resolve/main/adapter/README.md
- Command line
-
hf download hf://dirac-run/ec-0.6b/adapter/README.md
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curl -L -o README.md https://huggingface.co/dirac-run/ec-0.6b/resolve/main/adapter/README.md
Original EasyCommand 0.6B LoRA adapter
The cumulative trained A1 adapter, including the parent full epoch and 200 selected incremental updates. The binary is the preserved FP32 adapter.
Use original base Qwen/Qwen3-0.6B, pinned revision c1899de289a04d12100db370d81485cdf75e47ca. The adapter configuration contains that portable reference. Load this subfolder with PEFT and is_trainable=True to continue training. Do not apply it to the already merged EasyCommand weights.
The root model card includes loading examples, training history, evaluation boundaries and integrity instructions. Apache-2.0 applies, with attribution in NOTICE.