File size: 7,507 Bytes

---
datasets:
- cognitivecomputations/dolphin-r1
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- NousResearch/hermes-function-calling-v1
- AI-MO/NuminaMath-CoT
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-mixture
- cognitivecomputations/dolphin-coder
- HuggingFaceTB/smoltalk
- cognitivecomputations/samantha-data
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback
language:
- en
base_model: cognitivecomputations/Dolphin3.0-R1-Mistral-24B
pipeline_tag: text-generation
library_name: transformers
tags:
- TensorBlock
- GGUF
---

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## cognitivecomputations/Dolphin3.0-R1-Mistral-24B - GGUF

This repo contains GGUF format model files for [cognitivecomputations/Dolphin3.0-R1-Mistral-24B](https://huggingface.co/cognitivecomputations/Dolphin3.0-R1-Mistral-24B).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).

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## Prompt template

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Dolphin3.0-R1-Mistral-24B-Q2_K.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q2_K.gguf) | Q2_K | 8.890 GB | smallest, significant quality loss - not recommended for most purposes |
| [Dolphin3.0-R1-Mistral-24B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q3_K_S.gguf) | Q3_K_S | 10.400 GB | very small, high quality loss |
| [Dolphin3.0-R1-Mistral-24B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q3_K_M.gguf) | Q3_K_M | 11.474 GB | very small, high quality loss |
| [Dolphin3.0-R1-Mistral-24B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q3_K_L.gguf) | Q3_K_L | 12.401 GB | small, substantial quality loss |
| [Dolphin3.0-R1-Mistral-24B-Q4_0.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q4_0.gguf) | Q4_0 | 13.442 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Dolphin3.0-R1-Mistral-24B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q4_K_S.gguf) | Q4_K_S | 13.549 GB | small, greater quality loss |
| [Dolphin3.0-R1-Mistral-24B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q4_K_M.gguf) | Q4_K_M | 14.334 GB | medium, balanced quality - recommended |
| [Dolphin3.0-R1-Mistral-24B-Q5_0.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q5_0.gguf) | Q5_0 | 16.304 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Dolphin3.0-R1-Mistral-24B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q5_K_S.gguf) | Q5_K_S | 16.304 GB | large, low quality loss - recommended |
| [Dolphin3.0-R1-Mistral-24B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q5_K_M.gguf) | Q5_K_M | 16.764 GB | large, very low quality loss - recommended |
| [Dolphin3.0-R1-Mistral-24B-Q6_K.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q6_K.gguf) | Q6_K | 19.346 GB | very large, extremely low quality loss |
| [Dolphin3.0-R1-Mistral-24B-Q8_0.gguf](https://huggingface.co/tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF/blob/main/Dolphin3.0-R1-Mistral-24B-Q8_0.gguf) | Q8_0 | 25.055 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF --include "Dolphin3.0-R1-Mistral-24B-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:

```shell
huggingface-cli download tensorblock/Dolphin3.0-R1-Mistral-24B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```