Text Generation
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
smol-course
module_1
trl
sft
conversational
text-generation-inference
Instructions to use GetToasted/SmolLM2-FT-MyDataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GetToasted/SmolLM2-FT-MyDataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GetToasted/SmolLM2-FT-MyDataset") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("GetToasted/SmolLM2-FT-MyDataset") model = AutoModelForCausalLM.from_pretrained("GetToasted/SmolLM2-FT-MyDataset") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use GetToasted/SmolLM2-FT-MyDataset with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GetToasted/SmolLM2-FT-MyDataset" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GetToasted/SmolLM2-FT-MyDataset", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GetToasted/SmolLM2-FT-MyDataset
- SGLang
How to use GetToasted/SmolLM2-FT-MyDataset 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 "GetToasted/SmolLM2-FT-MyDataset" \ --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": "GetToasted/SmolLM2-FT-MyDataset", "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 "GetToasted/SmolLM2-FT-MyDataset" \ --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": "GetToasted/SmolLM2-FT-MyDataset", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use GetToasted/SmolLM2-FT-MyDataset with Docker Model Runner:
docker model run hf.co/GetToasted/SmolLM2-FT-MyDataset
File size: 3,744 Bytes
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"add_prefix_space": false,
"added_tokens_decoder": {
"0": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "<repo_name>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "<reponame>",
"lstrip": false,
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"rstrip": false,
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"special": true
},
"5": {
"content": "<file_sep>",
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"content": "<gh_stars>",
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},
"8": {
"content": "<issue_start>",
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"single_word": false,
"special": true
},
"9": {
"content": "<issue_comment>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"10": {
"content": "<issue_closed>",
"lstrip": false,
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"single_word": false,
"special": true
},
"11": {
"content": "<jupyter_start>",
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"single_word": false,
"special": true
},
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"content": "<jupyter_text>",
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},
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"content": "<jupyter_code>",
"lstrip": false,
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"single_word": false,
"special": true
},
"14": {
"content": "<jupyter_output>",
"lstrip": false,
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"special": true
},
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"content": "<jupyter_script>",
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},
"16": {
"content": "<empty_output>",
"lstrip": false,
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"special": true
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>"
],
"bos_token": "<|im_start|>",
"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 %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"model_max_length": 8192,
"pad_token": "<|im_end|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}
|