---
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