Instructions to use Azrail/smallm_350 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azrail/smallm_350 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azrail/smallm_350", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Azrail/smallm_350", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Azrail/smallm_350 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azrail/smallm_350" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azrail/smallm_350", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azrail/smallm_350
- SGLang
How to use Azrail/smallm_350 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 "Azrail/smallm_350" \ --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": "Azrail/smallm_350", "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 "Azrail/smallm_350" \ --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": "Azrail/smallm_350", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azrail/smallm_350 with Docker Model Runner:
docker model run hf.co/Azrail/smallm_350
Download tokenizer_config.json from Azrail/smallm_350: direct link, hf CLI and curl.
- Browser
- Download file 2.66 kB
-
https://huggingface.co/Azrail/smallm_350/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Azrail/smallm_350/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Azrail/smallm_350/resolve/main/tokenizer_config.json
2.66 kB
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "<|beginoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
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| "lstrip": false, | |
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| "single_word": false, | |
| "special": true | |
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| "lstrip": false, | |
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| "single_word": false, | |
| "special": true | |
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| "lstrip": false, | |
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| "lstrip": false, | |
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| }, | |
| "9": { | |
| "content": "<|reserved_token_8|>", | |
| "lstrip": false, | |
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| "special": true | |
| }, | |
| "10": { | |
| "content": "<|reserved_token_9|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
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| "special": true | |
| }, | |
| "11": { | |
| "content": "<|reserved_token_10|>", | |
| "lstrip": false, | |
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| "special": true | |
| } | |
| }, | |
| "bos_token": "<|beginoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "extra_special_tokens": {}, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "PreTrainedTokenizer" | |
| } | |