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

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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

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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.

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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

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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

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Logging & Checkpoints

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

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Data Processing

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

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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.

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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

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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

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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

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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.

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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

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Citation

If you use this model, please cite:

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

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Acknowledgements

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