The released GPT-2 large weights (774M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
Independent publisher
Rahul Iyer
rahuliyer
Models in Library5
Datasets in Library0
Models on Hugging Face5
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Published tasks: Text classification, Text generation.
Reported licenses: mit. Open a record to review its specific license and conditions.
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Models
The released GPT-2 medium weights (355M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
The released GPT-2 small weights (124M parameters) fine-tuned into a chat model on with the loss on assistant replies only. Small, medium and large versions are compared in the instruction-tuning write-up. What is the capital of France? - best.safetensors: the weights at the lowest validation loss (the final step). - config.json: the training config. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers, and a vocabulary of 50,263 tokens: GPT-2's plus six chat tokens. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository generatetext samples with temperature 1 and top-5 by default. The system message is optional. The…
The released GPT-2 small weights fine-tuned to classify AG News articles into four topics: World, Sports, Business and Sci/Tech. Four setups compare how much of the model needs to train. Training details and plots: Each setup trained for 3 epochs on one RTX 2080. Test numbers come from the checkpoint with the lowest validation loss. Each folder holds best.safetensors and its training config.json. logs/ has the training output of each run. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository The classifier reads the logits at each input's last real token. Inputs were…
GPT-2 small (124M parameters) trained from random initialisation on the FineWeb-Edu sample-10BT sample, with the model and training loop written directly in PyTorch. Training details and plots: - gpt2step018890.safetensors: the final weights. - config.json: the training config. - checkpoints/step.pt: the last three resume checkpoints, with optimizer and RNG state, for continuing training with pretraining/train.py --resume. These weights use the GPT2 class from gpt2-repro, not Hugging Face Transformers. Clone gpt2-repro and install its dependencies with uv, then run the code below from the repository It was trained on educational web text in English only and can produce factually wrong or…