Text Generation
Transformers
Safetensors
English
qwen3
bf16
bash
shell
command-generation
lora
easycommand
conversational
text-generation-inference
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 inference.json from dirac-run/ec-0.6b: direct link, hf CLI and curl.
- Browser
- Download file 639 Bytes
-
https://huggingface.co/dirac-run/ec-0.6b/resolve/main/inference.json
- Command line
-
hf download hf://dirac-run/ec-0.6b/inference.json
-
curl -L -o inference.json https://huggingface.co/dirac-run/ec-0.6b/resolve/main/inference.json
639 Bytes
| { | |
| "system_prompt_file": "system-prompt.txt", | |
| "system_prompt_sha256": "3a9028d5aebb73c3ed7363e63eb3e751689218775ea5572ab0dc6807e522238b", | |
| "format": "ChatML system/user/assistant", | |
| "add_bos": false, | |
| "parse_special": true, | |
| "enable_thinking": false, | |
| "qwen3_empty_thinking_prefix": true, | |
| "context_tokens": 2048, | |
| "max_new_tokens": 256, | |
| "eos_token_id": 151645, | |
| "sampling": "greedy", | |
| "generation_grammar": null, | |
| "cpu_threads": 8, | |
| "evaluated_backend": "native CPU decoder", | |
| "llama_cpp_revision": "1af554f8fc78ba029665a47b839484d9763e2a75", | |
| "output": { | |
| "kind": "COMMAND", | |
| "value": "Bash command string" | |
| } | |
| } | |