How to use from
SGLangUse 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 "Pingsz/pruned" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Pingsz/pruned",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
SmolLM-90M-Instruct-Pruned 🧠💡
A pruned version of HuggingFaceTB/SmolLM-135M-Instruct, reduced from 135M parameters to approximately 90M for faster inference and reduced memory usage, while maintaining reasonable performance for instruction-style tasks.
🔧 What’s Inside
- Base:
SmolLM-135M-Instruct - Parameters: ~90M
- Pruning method: Structured pruning (e.g., attention heads, MLP layers) using PyTorch/NVIDIA pruning tools (customize if needed).
- Vocabulary, tokenizer, and training objectives remain identical to the base model.
🚀 Intended Use
This model is optimized for:
- Low-latency applications
- Edge deployments
- Instruction-following tasks with compact models
- Use in environments with limited VRAM or compute
Example Use
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM-135M-Instruct")
model = AutoModelForCausalLM.from_pretrained("your-username/SmolLM-90M-Instruct-Pruned")
prompt = "Explain quantum computing to a 10-year-old."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Model tree for Pingsz/pruned
Base model
HuggingFaceTB/SmolLM-135M Quantized
HuggingFaceTB/SmolLM-135M-Instruct
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Pingsz/pruned" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pingsz/pruned", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'