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matthew-mcc_-_gpt2-finetuned-gguf
RichardErkhov/matthew-mcc_-_gpt2-finetuned-gguf
matthew-mcc_-_gpt2-finetuned-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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.gguf2.2 GB · 100%
At a glance
- Access
- Public
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024
- SHA
- ed36bdc9
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
gpt2-finetuned - GGUF
- Model creator: https://huggingface.co/matthew-mcc/
- Original model: https://huggingface.co/matthew-mcc/gpt2-finetuned/
| Name | Quant method | Size |
|---|---|---|
| gpt2-finetuned.Q2_K.gguf | Q2_K | 0.08GB |
| gpt2-finetuned.Q3_K_S.gguf | Q3_K_S | 0.08GB |
| gpt2-finetuned.Q3_K.gguf | Q3_K | 0.09GB |
| gpt2-finetuned.Q3_K_M.gguf | Q3_K_M | 0.09GB |
| gpt2-finetuned.Q3_K_L.gguf | Q3_K_L | 0.1GB |
| gpt2-finetuned.IQ4_XS.gguf | IQ4_XS | 0.1GB |
| gpt2-finetuned.Q4_0.gguf | Q4_0 | 0.1GB |
| gpt2-finetuned.IQ4_NL.gguf | IQ4_NL | 0.1GB |
| gpt2-finetuned.Q4_K_S.gguf | Q4_K_S | 0.1GB |
| gpt2-finetuned.Q4_K.gguf | Q4_K | 0.11GB |
| gpt2-finetuned.Q4_K_M.gguf | Q4_K_M | 0.11GB |
| gpt2-finetuned.Q4_1.gguf | Q4_1 | 0.11GB |
| gpt2-finetuned.Q5_0.gguf | Q5_0 | 0.11GB |
| gpt2-finetuned.Q5_K_S.gguf | Q5_K_S | 0.11GB |
| gpt2-finetuned.Q5_K.gguf | Q5_K | 0.12GB |
| gpt2-finetuned.Q5_K_M.gguf | Q5_K_M | 0.12GB |
| gpt2-finetuned.Q5_1.gguf | Q5_1 | 0.12GB |
| gpt2-finetuned.Q6_K.gguf | Q6_K | 0.13GB |
| gpt2-finetuned.Q8_0.gguf | Q8_0 | 0.17GB |
Original model description:
license: mit base_model: gpt2 tags:
- generated_from_trainer model-index:
- name: gpt2-finetuned 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-finetuned
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1255
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1625 | 1.0 | 1000 | 0.1287 |
| 0.1028 | 2.0 | 2000 | 0.1257 |
| 0.1491 | 3.0 | 3000 | 0.1255 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Created
- Nov 3, 2024
- Updated
- Nov 3, 2024