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Yugo55A-GGUF
datatab/Yugo55A-GGUF
Yugo55A-GGUF is a machine learning model from datatab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
- Developed by: datatab - License: MIT - Quantized from model : datatab/Yugo55A-GPT
All-time downloads
900
Public
Repo size
10.3 GB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.gguf10.3 GB · 100%
At a glance
- Library
- transformers
- License
- mit
- Model type
- mistral
- Access
- Public
- Created
- Mar 6, 2024
- Updated
- Mar 6, 2024
- SHA
- e978f988
Base models
Model card
From the Hugging Face model README
Yugo55A-GPT.GGUF
- Developed by: datatab
- License: MIT
- Quantized from model : datatab/Yugo55A-GPT
Full Weights Model
🏆 Results
<table> <tr> <th>MODEL</th> <th>ARC-E</th> <th>ARC-C</th> <th>Hellaswag</th> <th>BoolQ</th> <th>Winogrande</th> <th>OpenbookQA</th> <th>PiQA</th> </tr> <tr> <td><a href="https://huggingface.co/datatab/Yugo55-GPT-v4-4bit/">*Yugo55-GPT-v4-4bit</a></td> <td>51.41</td> <td>36.00</td> <td>57.51</td> <td>80.92</td> <td><strong>65.75</strong></td> <td>34.70</td> <td><strong>70.54</strong></td> </tr> <tr> <td><a href="https://huggingface.co/datatab/Yugo55A-GPT/">Yugo55A-GPT</a></td> <td><strong>51.52</strong></td> <td><strong>37.78</strong></td> <td><strong>57.52</strong></td> <td><strong>84.40</strong></td> <td>65.43</td> <td><strong>35.60</strong></td> <td>69.43</td> </tr> </table>Results obtained through the Serbian LLM evaluation, released by Aleksa Gordić: serbian-llm-eval
- Evaluation was conducted on a 4-bit version of the model due to hardware resource constraints.
Quant. preference
| Quant. | Description |
|---|---|
| not_quantized | Recommended. Fast conversion. Slow inference, big files. |
| fast_quantized | Recommended. Fast conversion. OK inference, OK file size. |
| quantized | Recommended. Slow conversion. Fast inference, small files. |
| f32 | Not recommended. Retains 100% accuracy, but super slow and memory hungry. |
| f16 | Fastest conversion + retains 100% accuracy. Slow and memory hungry. |
| q8_0 | Fast conversion. High resource use, but generally acceptable. |
| q4_k_m | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K |
| q5_k_m | Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K |
| q2_k | Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. |
| q3_k_l | Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K |
| q3_k_m | Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K |
| q3_k_s | Uses Q3_K for all tensors |
| q4_0 | Original quant method, 4-bit. |
| q4_1 | Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| q4_k_s | Uses Q4_K for all tensors |
| q4_k | alias for q4_k_m |
| q5_k | alias for q5_k_m |
| q5_0 | Higher accuracy, higher resource usage and slower inference. |
| q5_1 | Even higher accuracy, resource usage and slower inference. |
| q5_k_s | Uses Q5_K for all tensors |
| q6_k | Uses Q8_K for all tensors |
| iq2_xxs | 2.06 bpw quantization |
| iq2_xs | 2.31 bpw quantization |
| iq3_xxs | 3.06 bpw quantization |
| q3_k_xs | 3-bit extra small quantization |
- Library
- transformers
- Type
- mistral
- License
- mit
- Languages
- sr
- Created
- Mar 6, 2024
- Updated
- Mar 6, 2024