Instructions to use JetLM/SDAR-1.7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JetLM/SDAR-1.7B-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JetLM/SDAR-1.7B-Chat", 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("JetLM/SDAR-1.7B-Chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JetLM/SDAR-1.7B-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JetLM/SDAR-1.7B-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JetLM/SDAR-1.7B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JetLM/SDAR-1.7B-Chat
- SGLang
How to use JetLM/SDAR-1.7B-Chat 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 "JetLM/SDAR-1.7B-Chat" \ --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": "JetLM/SDAR-1.7B-Chat", "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 "JetLM/SDAR-1.7B-Chat" \ --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": "JetLM/SDAR-1.7B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JetLM/SDAR-1.7B-Chat with Docker Model Runner:
docker model run hf.co/JetLM/SDAR-1.7B-Chat
Commit History
-
v5-align: remove store_kv; always update() on cached forward
Verified signature0fe0818
-
Fix inv_freq zero-init on v5: register original_inv_freq as buffer, add compute_default_rope_parameters, delegate rotary _init_weights to super
Verified signature40f6a13 -
Force SDPA path (flash_attn had v5 compat issue with different Q/K seq lengths)
Verified signaturead90094
-
Restore store_kv vs retrieve-only split: use layers[idx] for v5 retrieve path
Verified signature5c82694 -
Use v5 DynamicCache.update() idiom instead of manual subscripting
Verified signatureb86b0ef -
Fix transformers v5 compatibility: rope default, DynamicCache access, optional pad_token_id
Verified signature4cc783b
-
remove LossKwargs
Verified signature2e309a6
-
Update README.md
Verified signaturee2fcc4b
-
Update README.md
Verified signature553d10a
-
Update README.md
Verified signatureddbde3b
-
Update README.md
Verified signature3588ef6 -
Update README.md
Verified signature5f8f25c -
Update README.md
Verified signature8f8e3ec
-
Update config.json
Verified signature77cc40e -
Add kernel to accelerate
Verified signature70f2069 -
Update README.md
Verified signature15cdedf -
Replace Qwen3 configs/modeling with SDAR version
Verified signaturece85381
-
Upload folder using huggingface_hub
Verified signaturea1f43ea -
Upload folder using huggingface_hub
Verified signature0b9412f -
Upload folder using huggingface_hub
Verified signature9b9a3a3 -
initial commit
Verified signature40c412d