metadata
license: mit
datasets:
- CreitinGameplays/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70Bmistral
language:
- en
base_model: CreitinGameplays/Mistral-Nemo-12B-R1-v0.1
pipeline_tag: text-generation
library_name: transformers
tags:
- TensorBlock
- GGUF
CreitinGameplays/Mistral-Nemo-12B-R1-v0.1 - GGUF
This repo contains GGUF format model files for CreitinGameplays/Mistral-Nemo-12B-R1-v0.1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4882.
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<s>[INST]You are an AI focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{reasoning}</think>{answer} - Process: Think first, then answer.
{prompt}[/INST]<think>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Mistral-Nemo-12B-R1-v0.1-Q2_K.gguf | Q2_K | 4.791 GB | smallest, significant quality loss - not recommended for most purposes |
| Mistral-Nemo-12B-R1-v0.1-Q3_K_S.gguf | Q3_K_S | 5.534 GB | very small, high quality loss |
| Mistral-Nemo-12B-R1-v0.1-Q3_K_M.gguf | Q3_K_M | 6.083 GB | very small, high quality loss |
| Mistral-Nemo-12B-R1-v0.1-Q3_K_L.gguf | Q3_K_L | 6.562 GB | small, substantial quality loss |
| Mistral-Nemo-12B-R1-v0.1-Q4_0.gguf | Q4_0 | 7.072 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Mistral-Nemo-12B-R1-v0.1-Q4_K_S.gguf | Q4_K_S | 7.120 GB | small, greater quality loss |
| Mistral-Nemo-12B-R1-v0.1-Q4_K_M.gguf | Q4_K_M | 7.477 GB | medium, balanced quality - recommended |
| Mistral-Nemo-12B-R1-v0.1-Q5_0.gguf | Q5_0 | 8.519 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Mistral-Nemo-12B-R1-v0.1-Q5_K_S.gguf | Q5_K_S | 8.519 GB | large, low quality loss - recommended |
| Mistral-Nemo-12B-R1-v0.1-Q5_K_M.gguf | Q5_K_M | 8.728 GB | large, very low quality loss - recommended |
| Mistral-Nemo-12B-R1-v0.1-Q6_K.gguf | Q6_K | 10.056 GB | very large, extremely low quality loss |
| Mistral-Nemo-12B-R1-v0.1-Q8_0.gguf | Q8_0 | 13.022 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Mistral-Nemo-12B-R1-v0.1-GGUF --include "Mistral-Nemo-12B-R1-v0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/Mistral-Nemo-12B-R1-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'