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BEGADE_-_chat-gguf
RichardErkhov/BEGADE_-_chat-gguf
BEGADE_-_chat-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.
All-time downloads
222
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Repo size
2.1 GB
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0
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Repo makeup
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.gguf2.4 GB · 100%
At a glance
- Access
- Public
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025
- SHA
- b342bc77
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
chat - GGUF
- Model creator: https://huggingface.co/BEGADE/
- Original model: https://huggingface.co/BEGADE/chat/
| Name | Quant method | Size |
|---|---|---|
| chat.Q2_K.gguf | Q2_K | 0.08GB |
| chat.IQ3_XS.gguf | IQ3_XS | 0.08GB |
| chat.IQ3_S.gguf | IQ3_S | 0.08GB |
| chat.Q3_K_S.gguf | Q3_K_S | 0.08GB |
| chat.IQ3_M.gguf | IQ3_M | 0.09GB |
| chat.Q3_K.gguf | Q3_K | 0.09GB |
| chat.Q3_K_M.gguf | Q3_K_M | 0.09GB |
| chat.Q3_K_L.gguf | Q3_K_L | 0.1GB |
| chat.IQ4_XS.gguf | IQ4_XS | 0.1GB |
| chat.Q4_0.gguf | Q4_0 | 0.1GB |
| chat.IQ4_NL.gguf | IQ4_NL | 0.1GB |
| chat.Q4_K_S.gguf | Q4_K_S | 0.1GB |
| chat.Q4_K.gguf | Q4_K | 0.11GB |
| chat.Q4_K_M.gguf | Q4_K_M | 0.11GB |
| chat.Q4_1.gguf | Q4_1 | 0.11GB |
| chat.Q5_0.gguf | Q5_0 | 0.11GB |
| chat.Q5_K_S.gguf | Q5_K_S | 0.11GB |
| chat.Q5_K.gguf | Q5_K | 0.12GB |
| chat.Q5_K_M.gguf | Q5_K_M | 0.12GB |
| chat.Q5_1.gguf | Q5_1 | 0.12GB |
| chat.Q6_K.gguf | Q6_K | 0.13GB |
| chat.Q8_0.gguf | Q8_0 | 0.17GB |
Original model description:
library_name: transformers tags:
- trl
- sft
- generated_from_trainer model-index:
- name: chat 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. -->
chat
This model was trained from scratch 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
- Created
- Feb 26, 2025
- Updated
- Feb 26, 2025