Text Generation
Transformers
Safetensors
domynedge
domyn
domyn-edge
long-context
agentic
tool-calling
sft
fully-open
conversational
custom_code
Instructions to use domyn/Domyn-Edge-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use domyn/Domyn-Edge-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="domyn/Domyn-Edge-SFT", 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("domyn/Domyn-Edge-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use domyn/Domyn-Edge-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "domyn/Domyn-Edge-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "domyn/Domyn-Edge-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/domyn/Domyn-Edge-SFT
- SGLang
How to use domyn/Domyn-Edge-SFT 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 "domyn/Domyn-Edge-SFT" \ --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": "domyn/Domyn-Edge-SFT", "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 "domyn/Domyn-Edge-SFT" \ --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": "domyn/Domyn-Edge-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use domyn/Domyn-Edge-SFT with Docker Model Runner:
docker model run hf.co/domyn/Domyn-Edge-SFT
Download tokenizer.json from domyn/Domyn-Edge-SFT: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/domyn/Domyn-Edge-SFT/resolve/main/tokenizer.json
- Command line
-
hf download hf://domyn/Domyn-Edge-SFT/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/domyn/Domyn-Edge-SFT/resolve/main/tokenizer.json
16 MB
- Xet hash:
- a245b6920cd2dd79895c8364ed7a25b9631ee3961af90db5408cd06dda807ed5
- Size of remote file:
- 16 MB
- SHA256:
- b674b69c9a68226dc8ca2a88a161ee1fd917dafc0361c442f8ec3d4073e967d2
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