Instructions to use akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset with Transformers:

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

pipe = pipeline("text-generation", model="akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset")
model = AutoModelForCausalLM.from_pretrained("akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset", device_map="auto")
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 Settings

vLLM

How to use akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset

SGLang

How to use akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset 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 "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset" \
    --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": "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset",
		"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 "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset" \
        --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": "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset with Docker Model Runner:
```
docker model run hf.co/akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset
```

Model Card: akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset

Model Description This model is a fine-tuned version of SmolLM2-360M using physics instruction datasets from both synthetic physics questions and camel-ai physics. It is optimized for solving college-level physics word problems, with LoRA-based parameter-efficient fine-tuning to reduce memory footprint and enable efficient deployment.

Base Model: HuggingFaceTB/SmolLM2-360M
Instruction Datasets: Combination of instruction-tuned camel-ai/physics and custom-generated synthetic datasets
Framework: transformers, trl, peft
Evaluation Task: MMLU – college_physics
Training Loss: ~0.14
Validation Loss: 0.48 (best at epoch 11)
Training Time: ~13,000 seconds
Steps: 4926 steps with batch size 4 and accumulation 4

Use Cases This model is intended for:

Physics education tools
College-level physics question answering
Low-resource inference applications (thanks to LoRA)

Training Details

Hardware: NVIDIA A5500 24GB
Precision: bf16 if available, else fp16
Optimizer: adamw_8bit
Epochs: 11
Scheduler: Linear
Learning Rate: 2e-5

Performance

Metric	Value
Training Loss	0.1419
Validation Loss	0.4764
Best Epoch	11
Train Samples/sec	24.42
Steps/sec	0.375
Total FLOPs	3.04e+17

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = "Question: What is the acceleration of an object with mass 5 kg under a force of 20 N?\nAnswer:"
print(pipe(prompt, max_new_tokens=100)[0]["generated_text"])

Limitations

Trained only on physics word problems – limited generalization outside this domain
May not generalize to multi-step derivations or non-instructional questions
Trained only on English prompts

Citation If you use this model, please cite:

@misc{vallala2025physmol,
  title={PhysmolLM: A Compact Large Language Model for Enhancing Physics Education on Mobile Devices},
  author={Akhil Vallala},
  year={2025},
  note={Master's Thesis, Florida Atlantic University}
}

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Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset

Base model

HuggingFaceTB/SmolLM2-360M

Finetuned

(118)

this model

akhilfau
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fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset

Model tree for akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset

Dataset used to train akhilfau/fine-tuned-smolLM2-360M-with-on-combined_Instruction_dataset