Text Generation
Transformers
Safetensors
English
surjo
feature-extraction
recurrent-transformer
linear-attention
gated-deltanet
custom-code
slm
conversational
custom_code
Instructions to use SurjoLabs/Surjo-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SurjoLabs/Surjo-100m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SurjoLabs/Surjo-100m", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SurjoLabs/Surjo-100m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SurjoLabs/Surjo-100m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SurjoLabs/Surjo-100m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurjoLabs/Surjo-100m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SurjoLabs/Surjo-100m
- SGLang
How to use SurjoLabs/Surjo-100m 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 "SurjoLabs/Surjo-100m" \ --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": "SurjoLabs/Surjo-100m", "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 "SurjoLabs/Surjo-100m" \ --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": "SurjoLabs/Surjo-100m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SurjoLabs/Surjo-100m with Docker Model Runner:
docker model run hf.co/SurjoLabs/Surjo-100m
Download optimizer_1.bin from SurjoLabs/Surjo-100m: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/SurjoLabs/Surjo-100m/resolve/main/optimizer_1.bin
- Command line
-
hf download hf://SurjoLabs/Surjo-100m/optimizer_1.bin
-
curl -L -o optimizer_1.bin https://huggingface.co/SurjoLabs/Surjo-100m/resolve/main/optimizer_1.bin
151 MB
- Xet hash:
- 0d43ac5c92c7791dcb5841165508b21edbd65f32cbba79c849a4b8d776563e9c
- Size of remote file:
- 151 MB
- SHA256:
- 58cd31daa16654980119e39767c298808a4433bbd82f24f4a359d1a73d5c8558
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