Text Generation
Transformers
Safetensors
English
llama
smollm2
smollm2-360m
distillation
Eval Results (legacy)
text-generation-inference
Instructions to use aloobun/d-SmolLM2-360M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloobun/d-SmolLM2-360M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aloobun/d-SmolLM2-360M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aloobun/d-SmolLM2-360M") model = AutoModelForCausalLM.from_pretrained("aloobun/d-SmolLM2-360M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aloobun/d-SmolLM2-360M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aloobun/d-SmolLM2-360M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aloobun/d-SmolLM2-360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aloobun/d-SmolLM2-360M
- SGLang
How to use aloobun/d-SmolLM2-360M 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 "aloobun/d-SmolLM2-360M" \ --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": "aloobun/d-SmolLM2-360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "aloobun/d-SmolLM2-360M" \ --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": "aloobun/d-SmolLM2-360M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aloobun/d-SmolLM2-360M with Docker Model Runner:
docker model run hf.co/aloobun/d-SmolLM2-360M
Download generation_config.json from aloobun/d-SmolLM2-360M: direct link, hf CLI and curl.
- Browser
- Download file 111 Bytes
-
https://huggingface.co/aloobun/d-SmolLM2-360M/resolve/4b51c7dcc5676a9c0cc4ef7dfd5bf8f7d2aa04d6/generation_config.json
- Command line
-
hf download hf://aloobun/d-SmolLM2-360M@4b51c7dcc5676a9c0cc4ef7dfd5bf8f7d2aa04d6/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/aloobun/d-SmolLM2-360M/resolve/4b51c7dcc5676a9c0cc4ef7dfd5bf8f7d2aa04d6/generation_config.json
111 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 0, | |
| "eos_token_id": 0, | |
| "transformers_version": "4.46.3" | |
| } | |