README.md

---
language:
  - en
license: other
base_model: HuggingFaceTB/SmolLM3-3B
tags:
  - sft
  - instruction-tuning
  - reasoning


  - long-context
  - fsdp
  - transformers
  - liger-kernel
  - english
datasets:
  - DGurgurov/Nemotron-Multilingual-Reasoning
metrics:
 pipeline_tag: text-generation
---

 # 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

 ```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": "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)

 ```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 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