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lonlo_-_model_troy-gguf
RichardErkhov/lonlo_-_model_troy-gguf
lonlo_-_model_troy-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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.gguf1.9 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 10, 2024
- Updated
- Aug 10, 2024
- SHA
- c108c6df
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
model_troy - GGUF
- Model creator: https://huggingface.co/lonlo/
- Original model: https://huggingface.co/lonlo/model_troy/
| Name | Quant method | Size |
|---|---|---|
| model_troy.Q2_K.gguf | Q2_K | 0.06GB |
| model_troy.IQ3_XS.gguf | IQ3_XS | 0.07GB |
| model_troy.IQ3_S.gguf | IQ3_S | 0.07GB |
| model_troy.Q3_K_S.gguf | Q3_K_S | 0.07GB |
| model_troy.IQ3_M.gguf | IQ3_M | 0.07GB |
| model_troy.Q3_K.gguf | Q3_K | 0.07GB |
| model_troy.Q3_K_M.gguf | Q3_K_M | 0.07GB |
| model_troy.Q3_K_L.gguf | Q3_K_L | 0.07GB |
| model_troy.IQ4_XS.gguf | IQ4_XS | 0.07GB |
| model_troy.Q4_0.gguf | Q4_0 | 0.08GB |
| model_troy.IQ4_NL.gguf | IQ4_NL | 0.08GB |
| model_troy.Q4_K_S.gguf | Q4_K_S | 0.08GB |
| model_troy.Q4_K.gguf | Q4_K | 0.08GB |
| model_troy.Q4_K_M.gguf | Q4_K_M | 0.08GB |
| model_troy.Q4_1.gguf | Q4_1 | 0.08GB |
| model_troy.Q5_0.gguf | Q5_0 | 0.09GB |
| model_troy.Q5_K_S.gguf | Q5_K_S | 0.09GB |
| model_troy.Q5_K.gguf | Q5_K | 0.09GB |
| model_troy.Q5_K_M.gguf | Q5_K_M | 0.09GB |
| model_troy.Q5_1.gguf | Q5_1 | 0.09GB |
| model_troy.Q6_K.gguf | Q6_K | 0.1GB |
| model_troy.Q8_0.gguf | Q8_0 | 0.12GB |
Original model description:
license: apache-2.0 base_model: distilgpt2 tags:
- generated_from_trainer model-index:
- name: model_troy 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. -->
model_troy
This model is a fine-tuned version of distilgpt2 on the None dataset.
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Created
- Aug 10, 2024
- Updated
- Aug 10, 2024