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Model: jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4
Source: Original Platform

README.md

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+---
+language:
+  - en
+license: apache-2.0
+library_name: transformers
+base_model: HuggingFaceTB/SmolLM2-1.7B
+pipeline_tag: text-generation
+tags:
+  - quantization
+  - qat
+  - quantization-aware-training
+  - scheduled-qat
+  - smollm2
+  - edge-deployment
+model_name: SmolLM2-1.7B Scheduled QAT Linear INT4
+datasets:
+  - wikitext
+---
+
+# SmolLM2-1.7B — Scheduled QAT (Linear Schedule)
+
+This model was produced by **Scheduled Quantization-Aware Training** with a linear precision reduction schedule, targeting INT4 deployment on edge devices (Android, iOS, Raspberry Pi).
+
+## Important
+
+**This model is in bfloat16 — it is NOT quantized.** QAT trains weights to be robust to quantization noise, but the actual quantization happens at export time. For the quantized GGUF versions ready for deployment, see:
+
+**[jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF](https://huggingface.co/jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF)**
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| **Base model** | [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B) |
+| **Method** | Scheduled QAT (Linear bit-width reduction) |
+| **Training data** | WikiText-103 (4000 sequences × 512 tokens) |
+| **Hardware** | Kaggle TPU v5e-8 (8 cores) |
+| **Epochs** | 1 |
+| **Effective batch size** | 64 (4 per-core × 2 grad accum × 8 cores) |
+| **Learning rate** | 2e-5 (cosine decay) |
+| **Optimizer** | AdamW (weight_decay=0.01) |
+| **Training precision** | bfloat16 |
+| **Training time** | ~1150 seconds |
+
+### Bit-Width Schedule
+
+| Phase | Epoch Range | Bit-width |
+|-------|------------|-----------|
+| Warmup | 0.0 → 0.1 | FP32 (no quantization noise) |
+| Linear reduction | 0.1 → 0.9 | 32 → 16 → 8 → 4 (gradual) |
+| Stabilization | 0.9 → 1.0 | INT4 (final fine-tuning) |
+
+### Results (WikiText-103 Test)
+
+| Metric | Value |
+|--------|-------|
+| Test loss | 3.0392 |
+| Test perplexity | 20.89 |
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4")
+
+inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `model.safetensors` | Model weights (bfloat16) |
+| `config.json` | Model architecture config |
+| `tokenizer.json` | Tokenizer |
+| `results.json` | Training results (loss, perplexity) |
+| `training_log.json` | Step-by-step training log |
+
+## Related
+
+- **GGUF (quantized, for deployment):** [jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF](https://huggingface.co/jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF)
+- **Base model:** [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B)
+
+## Citation
+
+This model is part of a thesis on Scheduled Quantization-Aware Training for Small Language Models targeting edge deployment.
+
+## License
+
+Apache 2.0 (same as base model)