Instructions to use toroe/SmolLM-3B-Science-EN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use toroe/SmolLM-3B-Science-EN with Transformers:

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

pipe = pipeline("text-generation", model="toroe/SmolLM-3B-Science-EN")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("toroe/SmolLM-3B-Science-EN")
model = AutoModelForCausalLM.from_pretrained("toroe/SmolLM-3B-Science-EN")
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 toroe/SmolLM-3B-Science-EN with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "toroe/SmolLM-3B-Science-EN"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "toroe/SmolLM-3B-Science-EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/toroe/SmolLM-3B-Science-EN

SGLang

How to use toroe/SmolLM-3B-Science-EN 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 "toroe/SmolLM-3B-Science-EN" \
    --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": "toroe/SmolLM-3B-Science-EN",
		"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 "toroe/SmolLM-3B-Science-EN" \
        --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": "toroe/SmolLM-3B-Science-EN",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use toroe/SmolLM-3B-Science-EN with Docker Model Runner:
```
docker model run hf.co/toroe/SmolLM-3B-Science-EN
```

SmolLM3-3B — English Reasoning Instruction Fine-Tune (Nemotron Multilingual Reasoning)

Model Description

This model is a Supervised Fine-Tuned (SFT) version of:

HuggingFaceTB/SmolLM3-3B

It was trained on the English (en) split of:

DGurgurov/Nemotron-Multilingual-Reasoning

The purpose of this fine-tune is to improve:

English instruction following
multi-step reasoning
long-context chat behavior

The dataset was converted into structured chat conversations and optimized using completion-only loss, meaning only the assistant’s responses contributed to the training objective.

Key Characteristics

Base model: SmolLM3-3B
Language: English specialization
Context length during training: 16,384 tokens
Chat formatted conversations
Packed sequences
Long-context reasoning tuning

Intended Uses

Suitable

Conversational assistants
Instruction-following agents
Reasoning tasks
Educational tutoring
Long-document Q&A
Research on small long-context LLMs

Not Suitable

Medical or legal advice
Autonomous decision making
Safety-critical systems
Financial decision automation

Training Data

Dataset:

DGurgurov/Nemotron-Multilingual-Reasoning

Processing configuration:

Language filter: English only
Converted to chat messages (prepare_messages=True)
Assistant-only loss masking (completion_only_loss=True)

User and system prompts were masked during training; only assistant tokens produced gradients.

Please consult the dataset card for data provenance and limitations.

Training Procedure

Training used HuggingFace Accelerate with Fully Sharded Data Parallel (FSDP) across 8 processes.

Core Setup

Method: Supervised fine-tuning (SFT)
Epochs: 3
Max sequence length: 16,384
Packing: enabled
Precision: bfloat16
Gradient checkpointing: enabled
Liger kernel: enabled
Distributed training: FSDP

Optimization

Optimizer: adamw_torch_fused
Batch size per device: 4
Gradient accumulation: 4
Effective batch size per GPU: 16 sequences / step
Weight decay: 0.05

Learning rate schedule:

Scheduler: cosine_with_min_lr
Warmup ratio: 0.05
Minimum learning rate: 5e-6

Logging & Checkpoints

Logging: every 5 steps
Checkpoint: every 450 steps
Tracking: Weights & Biases
Token accuracy logged during training

Data Processing

Dataset preprocessing workers: 16
Chat formatting: enabled
Dataset preparation: enabled
Language split: en

Usage

Transformers Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "YOUR_USERNAME/YOUR_MODEL_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
   model_id,
   device_map="auto",
   torch_dtype=torch.bfloat16,
)

messages = [
   {"role": "system", "content": "You are a helpful assistant."},
   {"role": "user", "content": "Explain why the sky is blue."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
   **inputs,
   max_new_tokens=512,
   temperature=0.7,
   top_p=0.9,
   do_sample=True,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Important:
Use apply_chat_template() when prompting. The model was trained on chat-formatted conversations and performance will degrade without it.

Evaluation

During training, token accuracy was logged as a diagnostic metric.

Token accuracy:

helps monitor training stability
is not a benchmark score
does not measure reasoning quality

For meaningful evaluation, use:

instruction-following benchmarks
reasoning datasets
long-context tasks

Limitations

May hallucinate incorrect information
Reasoning chains may contain logical mistakes
Performance near 16k tokens depends heavily on prompt structure
Smaller model → less world knowledge than large LLMs
Not suitable for safety-critical deployment

Bias & Safety

The model inherits biases from:

the base model
the training dataset

Recommended mitigations:

moderation filtering
safety-oriented system prompts
human oversight in sensitive use cases

License

This is a derivative model of:

HuggingFaceTB/SmolLM3-3B

The original base model license and restrictions apply, along with dataset terms.

Verify compatibility before commercial usage.

Reproducibility (Training Arguments)

accelerate launch --use_fsdp --num_processes 8 --config_file sft/my_config.yaml sft/sft_trainer.py

--model_name HuggingFaceTB/SmolLM3-3B
--tokenizer_name HuggingFaceTB/SmolLM3-3B
--dataset_path DGurgurov/Nemotron-Multilingual-Reasoning
--skip_prepare_dataset False
--lang_split en
--prepare_messages True
--completion_only_loss True
--max_length 16384

Citation

If you use this model, please cite:

HuggingFaceTB/SmolLM3-3B
DGurgurov/Nemotron-Multilingual-Reasoning

Acknowledgements

HuggingFaceTB — SmolLM3 base model
Nemotron Multilingual Reasoning dataset authors
HuggingFace Accelerate and Transformers libraries

Downloads last month: 8

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for toroe/SmolLM-3B-Science-EN

Base model

HuggingFaceTB/SmolLM3-3B-Base

Finetuned

HuggingFaceTB/SmolLM3-3B