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Jennny_-_gpt2-sft-gguf
RichardErkhov/Jennny_-_gpt2-sft-gguf
Jennny_-_gpt2-sft-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024
- SHA
- e56497d4
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
gpt2-sft - GGUF
- Model creator: https://huggingface.co/Jennny/
- Original model: https://huggingface.co/Jennny/gpt2-sft/
| Name | Quant method | Size |
|---|---|---|
| gpt2-sft.Q2_K.gguf | Q2_K | 0.17GB |
| gpt2-sft.Q3_K_S.gguf | Q3_K_S | 0.19GB |
| gpt2-sft.Q3_K.gguf | Q3_K | 0.21GB |
| gpt2-sft.Q3_K_M.gguf | Q3_K_M | 0.21GB |
| gpt2-sft.Q3_K_L.gguf | Q3_K_L | 0.23GB |
| gpt2-sft.IQ4_XS.gguf | IQ4_XS | 0.22GB |
| gpt2-sft.Q4_0.gguf | Q4_0 | 0.23GB |
| gpt2-sft.IQ4_NL.gguf | IQ4_NL | 0.23GB |
| gpt2-sft.Q4_K_S.gguf | Q4_K_S | 0.23GB |
| gpt2-sft.Q4_K.gguf | Q4_K | 0.25GB |
| gpt2-sft.Q4_K_M.gguf | Q4_K_M | 0.25GB |
| gpt2-sft.Q4_1.gguf | Q4_1 | 0.25GB |
| gpt2-sft.Q5_0.gguf | Q5_0 | 0.27GB |
| gpt2-sft.Q5_K_S.gguf | Q5_K_S | 0.27GB |
| gpt2-sft.Q5_K.gguf | Q5_K | 0.29GB |
| gpt2-sft.Q5_K_M.gguf | Q5_K_M | 0.29GB |
| gpt2-sft.Q5_1.gguf | Q5_1 | 0.29GB |
| gpt2-sft.Q6_K.gguf | Q6_K | 0.32GB |
| gpt2-sft.Q8_0.gguf | Q8_0 | 0.41GB |
Original model description:
license: mit base_model: openai-community/gpt2-medium tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer datasets:
- Anthropic/hh-rlhf model-index:
- name: gpt2-sft results: []
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
gpt2-sft
This model is a fine-tuned version of openai-community/gpt2-medium on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:
- Loss: 2.1977
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1966 | 1.0 | 1366 | 2.1977 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024