Instructions to use Jasonnn13/SmolLM2-FT-MyDataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jasonnn13/SmolLM2-FT-MyDataset with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Jasonnn13/SmolLM2-FT-MyDataset")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jasonnn13/SmolLM2-FT-MyDataset")
model = AutoModelForCausalLM.from_pretrained("Jasonnn13/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 Jasonnn13/SmolLM2-FT-MyDataset with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jasonnn13/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": "Jasonnn13/SmolLM2-FT-MyDataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jasonnn13/SmolLM2-FT-MyDataset

SGLang

How to use Jasonnn13/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 "Jasonnn13/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": "Jasonnn13/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 "Jasonnn13/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": "Jasonnn13/SmolLM2-FT-MyDataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jasonnn13/SmolLM2-FT-MyDataset with Docker Model Runner:
```
docker model run hf.co/Jasonnn13/SmolLM2-FT-MyDataset
```

SmolLM2-FT-MyDataset / README.md

Jasonnn13

End of training

36d744b verified about 1 month ago

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	---
	library_name: transformers
	license: apache-2.0
	base_model: HuggingFaceTB/SmolLM2-135M
	tags:
	- smol-course
	- module_1
	- trl
	- sft
	- trl
	- sft
	- generated_from_trainer
	model-index:
	- name: SmolLM2-FT-MyDataset
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# SmolLM2-FT-MyDataset

	This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9266

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 4
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- training_steps: 1000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 1.0161 \| 0.0982 \| 50 \| 1.0429 \|
	\| 0.9943 \| 0.1965 \| 100 \| 1.0086 \|
	\| 0.953 \| 0.2947 \| 150 \| 0.9906 \|
	\| 0.9561 \| 0.3929 \| 200 \| 0.9802 \|
	\| 0.9585 \| 0.4912 \| 250 \| 0.9699 \|
	\| 0.9394 \| 0.5894 \| 300 \| 0.9597 \|
	\| 0.9284 \| 0.6876 \| 350 \| 0.9535 \|
	\| 0.9237 \| 0.7859 \| 400 \| 0.9462 \|
	\| 0.8945 \| 0.8841 \| 450 \| 0.9402 \|
	\| 0.9043 \| 0.9823 \| 500 \| 0.9348 \|
	\| 0.7479 \| 1.0806 \| 550 \| 0.9421 \|
	\| 0.805 \| 1.1788 \| 600 \| 0.9388 \|
	\| 0.7359 \| 1.2770 \| 650 \| 0.9382 \|
	\| 0.7437 \| 1.3752 \| 700 \| 0.9365 \|
	\| 0.7778 \| 1.4735 \| 750 \| 0.9336 \|
	\| 0.7678 \| 1.5717 \| 800 \| 0.9317 \|
	\| 0.7452 \| 1.6699 \| 850 \| 0.9301 \|
	\| 0.7373 \| 1.7682 \| 900 \| 0.9279 \|
	\| 0.738 \| 1.8664 \| 950 \| 0.9269 \|
	\| 0.7513 \| 1.9646 \| 1000 \| 0.9266 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.10.0+cu128
	- Datasets 4.0.0
	- Tokenizers 0.19.1