Create README.md

README.md

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+---
+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