Text Generation
Transformers
Safetensors
English
German
smallm
small
125m
bilingual
english
german
conversational
custom_code
Instructions to use He-Tag/smallm-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use He-Tag/smallm-125m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="He-Tag/smallm-125m", 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("He-Tag/smallm-125m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use He-Tag/smallm-125m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "He-Tag/smallm-125m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "He-Tag/smallm-125m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/He-Tag/smallm-125m
- SGLang
How to use He-Tag/smallm-125m 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 "He-Tag/smallm-125m" \ --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": "He-Tag/smallm-125m", "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 "He-Tag/smallm-125m" \ --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": "He-Tag/smallm-125m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use He-Tag/smallm-125m with Docker Model Runner:
docker model run hf.co/He-Tag/smallm-125m
Download tokenizer_config.json from He-Tag/smallm-125m: direct link, hf CLI and curl.
- Browser
- Download file 447 Bytes
-
https://huggingface.co/He-Tag/smallm-125m/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://He-Tag/smallm-125m/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/He-Tag/smallm-125m/resolve/main/tokenizer_config.json
447 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "eos_token": "<|im_end|>", | |
| "pad_token": "<|endoftext|>", | |
| "unk_token": null, | |
| "bos_token": null, | |
| "model_max_length": 1024, | |
| "clean_up_tokenization_spaces": false, | |
| "chat_template": "{% for message in messages %}{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" | |
| } | |