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Model: toroe/SmolLM-3B-Science-ES Source: Original Platform
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README.md
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README.md
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---
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language:
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- es
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license: other
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base_model: HuggingFaceTB/SmolLM3-3B
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tags:
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- sft
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- instruction-tuning
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- reasoning
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- long-context
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- spanish
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- fsdp
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- transformers
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- liger-kernel
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datasets:
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- DGurgurov/Nemotron-Multilingual-Reasoning
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metrics:
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- token_accuracy
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library_name: transformers
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pipeline_tag: text-generation
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---
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# SmolLM3-3B — Spanish Reasoning Instruction Fine-Tune (Nemotron Multilingual Reasoning)
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## Model Description
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This model is a **Supervised Fine-Tuned (SFT)** version of:
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`HuggingFaceTB/SmolLM3-3B`
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Fine-tuned on the **Spanish (`es`) split** of:
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`DGurgurov/Nemotron-Multilingual-Reasoning`
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The goal of this training run was to improve:
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- Spanish instruction following
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- multi-step reasoning
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- conversational behavior
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- long-context understanding
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Training used structured chat conversations and **completion-only loss**, meaning only the assistant responses were optimized.
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### Key Characteristics
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- Base model: SmolLM3-3B
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- Language specialization: Spanish
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- Context length during training: **16,384 tokens**
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- Chat-format training
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- Packed sequences
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- Long-context reasoning tuning
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---
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## Intended Uses
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### Suitable
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- Spanish conversational assistants
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- tutoring or educational assistants
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- reasoning and explanation tasks
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- document question answering
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- research on efficient small LLMs
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### Not Suitable
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- legal or medical advice
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- autonomous decision making
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- safety-critical systems
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- high-risk financial use
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---
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## Training Data
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Dataset:
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`DGurgurov/Nemotron-Multilingual-Reasoning`
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Processing configuration:
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- Language filter: **Spanish only**
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- Converted to chat messages (`prepare_messages=True`)
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- Assistant-only optimization (`completion_only_loss=True`)
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User and system messages were masked during training.
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Consult the dataset card for data sources and limitations.
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---
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## Training Procedure
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Training was performed using **HuggingFace Accelerate with Fully Sharded Data Parallel (FSDP)** across 8 processes.
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### Core Setup
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- Method: Supervised fine-tuning (SFT)
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- Epochs: **3**
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- Maximum sequence length: **16,384 tokens**
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- Sequence packing: enabled
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- Precision: **bfloat16**
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- Gradient checkpointing: enabled
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- Liger kernel: enabled
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- Distributed training: FSDP
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---
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### Optimization
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- Optimizer: `adamw_torch_fused`
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- Batch size per device: 4
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- Gradient accumulation steps: 4
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- Effective batch size per GPU: 16 sequences per step
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- Weight decay: 0.05
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Learning rate schedule:
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- Scheduler: `cosine_with_min_lr`
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- Warmup ratio: 0.05
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- Minimum LR: 5e-6
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---
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### Logging & Checkpoints
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- Logging every 5 steps
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- Checkpoint every 450 steps
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- Weights & Biases tracking
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- Token accuracy logged during training
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---
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### Data Processing
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- Dataset preprocessing workers: 16
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- Chat formatting enabled
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- Dataset preparation enabled
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- Language split: `es`
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---
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## Usage
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### Transformers Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "YOUR_USERNAME/YOUR_MODEL_REPO"
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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messages = [
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{"role": "system", "content": "Eres un asistente útil."},
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{"role": "user", "content": "¿Por qué el cielo es azul?"}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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**Important:**
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Use `apply_chat_template()` when prompting. The model was trained on chat-formatted conversations and performance will degrade without it.
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---
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## Evaluation
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During training, **token accuracy** was logged as a diagnostic metric.
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Token accuracy:
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- monitors training stability
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- is **not** a benchmark
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- does not measure reasoning ability
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For meaningful evaluation, use:
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- instruction-following benchmarks
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- reasoning datasets
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- long-context tasks
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---
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## Limitations
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- May hallucinate incorrect information
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- Reasoning chains may contain logical errors
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- Performance near 16k tokens depends heavily on prompt structure
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- Smaller model → weaker world knowledge than larger LLMs
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- Not suitable for safety-critical deployment
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---
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## Bias & Safety
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The model inherits biases from:
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- the base model
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- the training dataset
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Recommended mitigations:
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- moderation filtering
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- safety-oriented system prompts
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- human review for sensitive applications
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---
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## License
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This is a derivative model of:
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`HuggingFaceTB/SmolLM3-3B`
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The original base model license and restrictions apply, along with dataset terms.
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Verify compatibility before commercial use.
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---
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## Reproducibility (Training Arguments)
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```text
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accelerate launch --use_fsdp --num_processes 8 --config_file sft/my_config.yaml sft/sft_trainer.py
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--model_name HuggingFaceTB/SmolLM3-3B
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--tokenizer_name HuggingFaceTB/SmolLM3-3B
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--dataset_path DGurgurov/Nemotron-Multilingual-Reasoning
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--skip_prepare_dataset False
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--lang_split es
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--prepare_messages True
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--completion_only_loss True
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--max_length 16384
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--dataset_num_proc 16
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--packing True
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--use_liger_kernel True
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--bf16 True
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--log_token_accuracy True
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--optim adamw_torch_fused
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--gradient_checkpointing True
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--per_device_train_batch_size 4
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--gradient_accumulation_steps 4
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--ddp_find_unused_parameters False
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--lr_scheduler_type cosine_with_min_lr
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--lr_scheduler_kwargs {"min_lr": 5.0e-6}
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--warmup_ratio 0.05
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--weight_decay 0.05
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--report_to wandb
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--run_name smol_3b_3epochs_lns_es
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--num_train_epochs 3
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--save_strategy steps
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--logging_steps 5
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--save_steps 450
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```
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---
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## Citation
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If you use this model, please cite:
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- `HuggingFaceTB/SmolLM3-3B`
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- `DGurgurov/Nemotron-Multilingual-Reasoning`
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---
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## Acknowledgements
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- HuggingFaceTB — SmolLM3 base model
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- Nemotron Multilingual Reasoning dataset authors
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- HuggingFace Accelerate and Transformers libraries
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