SmolLM-3B-Science-ES/README.md

---
language:
  - es
license: other
base_model: HuggingFaceTB/SmolLM3-3B
tags:
  - sft
  - instruction-tuning
  - reasoning
  - long-context
  - spanish
  - fsdp
  - transformers
  - liger-kernel
datasets:
  - DGurgurov/Nemotron-Multilingual-Reasoning
metrics:
  - token_accuracy
library_name: transformers
pipeline_tag: text-generation
---

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

## Model Description

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

`HuggingFaceTB/SmolLM3-3B`

Fine-tuned on the **Spanish (`es`) split** of:

`DGurgurov/Nemotron-Multilingual-Reasoning`

The goal of this training run was to improve:

- Spanish instruction following
- multi-step reasoning
- conversational behavior
- long-context understanding

Training used structured chat conversations and **completion-only loss**, meaning only the assistant responses were optimized.

### Key Characteristics

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

---

## Intended Uses

### Suitable
- Spanish conversational assistants
- tutoring or educational assistants
- reasoning and explanation tasks
- document question answering
- research on efficient small LLMs

### Not Suitable
- legal or medical advice
- autonomous decision making
- safety-critical systems
- high-risk financial use

---

## Training Data

Dataset:

`DGurgurov/Nemotron-Multilingual-Reasoning`

Processing configuration:

- Language filter: **Spanish only**
- Converted to chat messages (`prepare_messages=True`)
- Assistant-only optimization (`completion_only_loss=True`)

User and system messages were masked during training.

Consult the dataset card for data sources and limitations.

---

## Training Procedure

Training was performed using **HuggingFace Accelerate with Fully Sharded Data Parallel (FSDP)** across 8 processes.

### Core Setup

- Method: Supervised fine-tuning (SFT)
- Epochs: **3**
- Maximum sequence length: **16,384 tokens**
- Sequence 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 steps: 4
- Effective batch size per GPU: 16 sequences per step
- Weight decay: 0.05

Learning rate schedule:

- Scheduler: `cosine_with_min_lr`
- Warmup ratio: 0.05
- Minimum LR: 5e-6

---

### Logging & Checkpoints

- Logging every 5 steps
- Checkpoint every 450 steps
- Weights & Biases tracking
- Token accuracy logged during training

---

### Data Processing

- Dataset preprocessing workers: 16
- Chat formatting enabled
- Dataset preparation enabled
- Language split: `es`

---

## Usage

### Transformers Example

```python
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": "Eres un asistente útil."},
    {"role": "user", "content": "¿Por qué el cielo es azul?"}
]

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:
- monitors training stability
- is **not** a benchmark
- does not measure reasoning ability

For meaningful evaluation, use:
- instruction-following benchmarks
- reasoning datasets
- long-context tasks

---

## Limitations

- May hallucinate incorrect information
- Reasoning chains may contain logical errors
- Performance near 16k tokens depends heavily on prompt structure
- Smaller model → weaker world knowledge than larger 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 review for sensitive applications

---

## 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 use.

---

## Reproducibility (Training Arguments)

```text
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 es
--prepare_messages True
--completion_only_loss True
--max_length 16384
--dataset_num_proc 16
--packing True
--use_liger_kernel True
--bf16 True
--log_token_accuracy True
--optim adamw_torch_fused
--gradient_checkpointing True
--per_device_train_batch_size 4
--gradient_accumulation_steps 4
--ddp_find_unused_parameters False
--lr_scheduler_type cosine_with_min_lr
--lr_scheduler_kwargs {"min_lr": 5.0e-6}
--warmup_ratio 0.05
--weight_decay 0.05
--report_to wandb
--run_name smol_3b_3epochs_lns_es
--num_train_epochs 3
--save_strategy steps
--logging_steps 5
--save_steps 450
```
---

## 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