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Model: francescofiamingo1/FF_3.1
Source: Original Platform

README.md

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+---
+language:
+- en
+- it
+license: apache-2.0
+tags:
+- text-generation
+- gpt2
+- decoder-only
+- causal-lm
+- distillation
+- dpo
+- sft
+- lora
+library_name: transformers
+pipeline_tag: text-generation
+model-index:
+- name: FF_3.1
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU
+      type: cais/mmlu
+    metrics:
+    - type: accuracy
+      value: 27.94
+      name: 5-shot accuracy
+---
+
+# FF_3.1
+
+**FF_3.1** is a 2.02B parameter GPT-2 decoder-only language model trained from scratch with a multi-stage pipeline combining supervised fine-tuning, preference optimization, knowledge distillation, and instruction tuning.
+
+## Model Details
+
+| | |
+|---|---|
+| **Architecture** | GPT-2 decoder-only |
+| **Parameters** | 2.02B |
+| **Hidden size (d)** | 2048 |
+| **Attention heads (h)** | 16 |
+| **FFN size (ff)** | 8192 |
+| **Layers (L)** | 38 |
+| **Context length** | 2048 |
+| **Tokenizer** | GPT-2 BPE (vocab size: 50,257) |
+| **Precision** | bfloat16 |
+
+## Training Pipeline
+
+FF_3.1 was trained through a 5-stage pipeline:
+
+1. **Pretraining** — 90B tokens on a large English corpus
+2. **SFT** — 760K + 100K examples (OpenHermes-2.5 / NuminaMath / Eurus)
+3. **DPO** — 38,863 preference pairs
+4. **Distillation v3** — 47K examples targeting MMLU + GSM8K + ARC benchmarks
+5. **LoRA v4b** — 10K examples for instruction following refinement
+
+## Evaluation
+
+| Benchmark | Score |
+|---|---|
+| **MMLU (5-shot)** | **27.94%** (+3.94 pp vs FF_3 baseline of 24%) |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("francescofiamingo1/FF_3.1", torch_dtype="bfloat16")
+tokenizer = AutoTokenizer.from_pretrained("francescofiamingo1/FF_3.1")
+
+input_text = "Explain photosynthesis in simple terms."
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Known Limitations
+
+- **Math reasoning** is still weak — the model struggles with multi-step arithmetic and word problems
+- **Instruction count following** is imprecise — the model may not reliably follow constraints like "list exactly 5 items"
+
+## What's Next
+
+**FF_3.2** will focus on:
+- DPO with UltraFeedback dataset for improved preference alignment
+- Improved math dataset for stronger quantitative reasoning
+
+## License
+
+Apache 2.0