77 lines
3.3 KiB
Markdown
77 lines
3.3 KiB
Markdown
---
|
|
license: apache-2.0
|
|
base_model: mistralai/Ministral-8B-Instruct-2410
|
|
tags:
|
|
- gguf
|
|
- quantized
|
|
- mistral
|
|
- ministral
|
|
- text-generation
|
|
language:
|
|
- en
|
|
pipeline_tag: text-generation
|
|
---
|
|
|
|
# Ministral-8B-Instruct — GGUF Quants
|
|
|
|
Quantized GGUF versions of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410) — Mistral AI's Ministral 8B instruct model, optimized for edge and on-device deployment. Features sliding window attention for efficient long-context processing.
|
|
|
|
## Available Files
|
|
|
|
| File | Quant | Size | Use Case |
|
|
|------|-------|------|----------|
|
|
| `Ministral-8B-Instruct-Q8_0.gguf` | Q8_0 | ~8.5GB | Maximum quality |
|
|
| `Ministral-8B-Instruct-Q6_K.gguf` | Q6_K | ~6.6GB | Near-lossless |
|
|
| `Ministral-8B-Instruct-Q5_K_M.gguf` | Q5_K_M | ~5.7GB | High quality |
|
|
| `Ministral-8B-Instruct-Q4_K_M.gguf` | Q4_K_M | ~4.9GB | **Recommended default** |
|
|
| `Ministral-8B-Instruct-Q3_K_M.gguf` | Q3_K_M | ~3.9GB | Low VRAM |
|
|
| `Ministral-8B-Instruct-IQ4_XS.gguf` | IQ4_XS | ~4.3GB | Imatrix 4-bit |
|
|
| `Ministral-8B-Instruct-IQ3_XXS.gguf` | IQ3_XXS | ~3.2GB | Imatrix 3-bit |
|
|
| `Ministral-8B-Instruct-IQ2_M.gguf` | IQ2_M | ~2.8GB | Imatrix 2-bit |
|
|
| `Ministral-8B-Instruct-IQ1_S.gguf` | IQ1_S | ~2.0GB | Extreme compression |
|
|
| `Ministral-8B-Instruct-fp16.gguf` | FP16 | ~16.0GB | Full precision |
|
|
| `imatrix.dat` | — | — | Importance matrix |
|
|
|
|
## Usage
|
|
|
|
```bash
|
|
./llama-cli -m Ministral-8B-Instruct-Q4_K_M.gguf \
|
|
--ctx-size 8192 -n 512 \
|
|
-p "[INST] Hello! [/INST]"
|
|
|
|
ollama run hf.co/DuoNeural/Ministral-8B-Instruct-GGUF:Q4_K_M
|
|
```
|
|
|
|
- **Parameters**: 8B | **License**: Apache 2.0 | **Context**: 32K (SWA)
|
|
|
|
---
|
|
|
|
*Quantized by DuoNeural using llama.cpp on RTX 5090.*
|
|
|
|
---
|
|
|
|
## DuoNeural
|
|
|
|
**DuoNeural** is an open AI research lab — human + AI in collaboration.
|
|
|
|
| Platform | Link |
|
|
|----------|------|
|
|
| HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
|
|
| Website | [duoneural.com](https://duoneural.com) |
|
|
| GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
|
|
| X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
|
|
| Email | duoneural@proton.me |
|
|
| Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
|
|
| Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
|
|
|
|
### DuoNeural Research Publications
|
|
|
|
| Title | DOI |
|
|
|-------|-----|
|
|
| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
|
|
| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
|
|
| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
|
|
| [The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems](https://doi.org/10.5281/zenodo.19952612) | [10.5281/zenodo.19952612](https://doi.org/10.5281/zenodo.19952612) |
|
|
|
|
*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*
|