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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

SmolLM2-FT-MyDataset

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.5380

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: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 3
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
1.3859	1.1538	50	1.8652
1.2355	2.3077	100	1.9235
1.1499	3.4615	150	1.9949
1.0908	4.6154	200	2.0660
0.9575	5.7692	250	2.1495
0.8631	6.9231	300	2.2484
0.7775	8.0769	350	2.3646
0.6162	9.2308	400	2.5319
0.5879	10.3846	450	2.6362
0.5224	11.5385	500	2.7548
0.4339	12.6923	550	2.8590
0.4305	13.8462	600	2.9796
0.3826	15.0	650	3.0848
0.3383	16.1538	700	3.2293
0.2905	17.3077	750	3.3046
0.2581	18.4615	800	3.3902
0.2367	19.6154	850	3.4507
0.2223	20.7692	900	3.4877
0.2136	21.9231	950	3.5117
0.217	23.0769	1000	3.5380

Framework versions

Transformers 4.44.2
Pytorch 2.8.0+cu126
Datasets 4.2.0
Tokenizers 0.19.1

Downloads last month: 1

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for chairsarecats/SmolLM2-FT-MyDataset

Base model

HuggingFaceTB/SmolLM2-135M

Finetuned

(906)

this model