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ff-125m-cloud
francescofiamingo1/ff-125m-cloud
ff-125m-cloud is a machine learning model from francescofiamingo1. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Author: Francesco Fiamingo Base architecture: GPT-2-like (125 M parameters) Training dataset: English CC-News (cleaned & deduplicated) Tokenizer: ByteLevel BPE – 32 k vocab Training hardware: NVIDIA A100 80 GB (RunPod…
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At a glance
- Model type
- gpt2
- Access
- Public
- Created
- Nov 11, 2025
- Updated
- Nov 11, 2025
- SHA
- f095745e
Model card
From the Hugging Face model README
FF-125M (Cloud Trained)
Author: Francesco Fiamingo
Base architecture: GPT-2-like (125 M parameters)
Training dataset: English CC-News (cleaned & deduplicated)
Tokenizer: ByteLevel BPE – 32 k vocab
Training hardware: NVIDIA A100 80 GB (RunPod)
Loss function: Causal Language Modeling
Final Perplexity: ≈ 2.8
Frameworks: PyTorch | Transformers | W&B | AWS S3
🧠 Model Overview
FF-125M is a compact, didactic LLM trained from scratch to reproduce
GPT-2-style behavior while maintaining modern training stability.
It serves as the baseline for the upcoming FF-7B bilingual model.
⚙️ Usage Example
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
model = GPT2LMHeadModel.from_pretrained("francescofiamingo1/ff-125m-cloud")
tokenizer = GPT2TokenizerFast.from_pretrained("francescofiamingo1/ff-125m-cloud")
prompt = "In a surprising discovery,"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=60)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Type
- gpt2
- Created
- Nov 11, 2025
- Updated
- Nov 11, 2025