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MistThena7BV2-GGUF
N-Bot-Int/MistThena7BV2-GGUF
MistThena7BV2-GGUF is a machine learning model from N-Bot-Int. 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 apache-2.0.
- [](https://ko-fi.com/J3J61D8NHV) - https://ko-fi.com/nexusnetworkint
All-time downloads
970
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Repo size
17.2 GB
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3
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.gguf17.2 GB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- mistral
- Access
- Public
- Created
- Apr 13, 2025
- Updated
- Apr 15, 2025
- SHA
- 1f2811b8
Base models
Model card
From the Hugging Face model README
Support Us Through
- [https://ko-fi.com/nexusnetworkint](Official Ko-FI link!)
GGUF Version
GGUF with Quants! Allowing you to run models using KoboldCPP and other AI Environments!
Quantizations:
| Quant Type | Benefits | Cons |
|---|---|---|
| Q4_K_M | ✅ Smallest size (fastest inference) | ❌ Lowest accuracy compared to other quants |
| ✅ Requires the least VRAM/RAM | ❌ May struggle with complex reasoning | |
| ✅ Ideal for edge devices & low-resource setups | ❌ Can produce slightly degraded text quality | |
| Q5_K_M | ✅ Better accuracy than Q4, while still compact | ❌ Slightly larger model size than Q4 |
| ✅ Good balance between speed and precision | ❌ Needs a bit more VRAM than Q4 | |
| ✅ Works well on mid-range GPUs | ❌ Still not as accurate as higher-bit models | |
| Q8_0 | ✅ Highest accuracy (closest to full model) | ❌ Requires significantly more VRAM/RAM |
| ✅ Best for complex reasoning & detailed outputs | ❌ Slower inference compared to Q4 & Q5 | |
| ✅ Suitable for high-end GPUs & serious workloads | ❌ Larger file size (takes more storage) |
Model Details:
Read the Model details on huggingface Model Detail Here!
- Library
- transformers
- Type
- mistral
- License
- apache-2.0
- Languages
- en
- Created
- Apr 13, 2025
- Updated
- Apr 15, 2025