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Model: jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4 Source: Original Platform
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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base_model: HuggingFaceTB/SmolLM2-1.7B
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pipeline_tag: text-generation
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tags:
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- quantization
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- qat
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- quantization-aware-training
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- scheduled-qat
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- smollm2
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- edge-deployment
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model_name: SmolLM2-1.7B Scheduled QAT Linear INT4
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datasets:
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- wikitext
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---
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# SmolLM2-1.7B — Scheduled QAT (Linear Schedule)
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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).
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## Important
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**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:
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**[jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF](https://huggingface.co/jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF)**
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## Training Details
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| Parameter | Value |
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|-----------|-------|
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| **Base model** | [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B) |
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| **Method** | Scheduled QAT (Linear bit-width reduction) |
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| **Training data** | WikiText-103 (4000 sequences × 512 tokens) |
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| **Hardware** | Kaggle TPU v5e-8 (8 cores) |
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| **Epochs** | 1 |
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| **Effective batch size** | 64 (4 per-core × 2 grad accum × 8 cores) |
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| **Learning rate** | 2e-5 (cosine decay) |
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| **Optimizer** | AdamW (weight_decay=0.01) |
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| **Training precision** | bfloat16 |
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| **Training time** | ~1150 seconds |
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### Bit-Width Schedule
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| Phase | Epoch Range | Bit-width |
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|-------|------------|-----------|
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| Warmup | 0.0 → 0.1 | FP32 (no quantization noise) |
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| Linear reduction | 0.1 → 0.9 | 32 → 16 → 8 → 4 (gradual) |
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| Stabilization | 0.9 → 1.0 | INT4 (final fine-tuning) |
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### Results (WikiText-103 Test)
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| Metric | Value |
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|--------|-------|
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| Test loss | 3.0392 |
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| Test perplexity | 20.89 |
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-INT4")
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inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Files
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| File | Description |
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|------|-------------|
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| `model.safetensors` | Model weights (bfloat16) |
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| `config.json` | Model architecture config |
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| `tokenizer.json` | Tokenizer |
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| `results.json` | Training results (loss, perplexity) |
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| `training_log.json` | Step-by-step training log |
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## Related
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- **GGUF (quantized, for deployment):** [jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF](https://huggingface.co/jpcurada/SmolLM2-1.7B-Scheduled-QAT-Linear-GGUF)
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- **Base model:** [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B)
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## Citation
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This model is part of a thesis on Scheduled Quantization-Aware Training for Small Language Models targeting edge deployment.
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## License
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Apache 2.0 (same as base model)
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 130000,
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"tie_word_embeddings": true,
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"transformers_version": "4.57.1",
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"use_cache": true,
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"vocab_size": 49152
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}
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generation_config.json
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results.json
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{
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"method": "scheduled_qat_linear",
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"model": "HuggingFaceTB/SmolLM2-1.7B",
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"target_bits": 4,
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}
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vocab.json
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