Plana-Chan/dolphin-2.9-llama3-8b-GGUF overview

---

license: other

base_model: cognitivecomputations/dolphin-2.9-llama3-8b

tags:

• generated_from_trainer
• axolotl
• TensorBlock
• GGUF

datasets:

• cognitivecomputations/Dolphin-2.9
• teknium/OpenHermes-2.5
• m-a-p/CodeFeedback-Filtered-Instruction
• cognitivecomputations/dolphin-coder
• cognitivecomputations/samantha-data
• HuggingFaceH4/ultrachat_200k
• microsoft/orca-math-word-problems-200k
• abacusai/SystemChat-1.1
• Locutusque/function-calling-chatml
• internlm/Agent-FLAN

model-index:

• name: out

results: []

---

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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">

</div>






cognitivecomputations/dolphin-2.9-llama3-8b - GGUF

This repo contains GGUF format model files for cognitivecomputations/dolphin-2.9-llama3-8b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Our projects

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<th colspan="2" style="font-size: 25px;">Forge</th>

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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>

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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>

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<th colspan="2">

<a href="https://github.com/TensorBlock/forge" target="_blank" style="

display: inline-block;

padding: 8px 16px;

background-color: #FF7F50;

color: white;

text-decoration: none;

border-radius: 6px;

font-weight: bold;

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">🚀 Try it now! 🚀</a>

</th>

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<th style="font-size: 25px;">Awesome MCP Servers</th>

<th style="font-size: 25px;">TensorBlock Studio</th>

</tr>

<tr>

<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>

<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>

</tr>

<tr>

<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>

<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>

</tr>

<tr>

<th>

<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="

display: inline-block;

padding: 8px 16px;

background-color: #FF7F50;

color: white;

text-decoration: none;

border-radius: 6px;

font-weight: bold;

font-family: sans-serif;

">👀 See what we built 👀</a>

</th>

<th>

<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="

display: inline-block;

padding: 8px 16px;

background-color: #FF7F50;

color: white;

text-decoration: none;

border-radius: 6px;

font-weight: bold;

font-family: sans-serif;

">👀 See what we built 👀</a>

</th>

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</table>

Prompt template

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

Model file specification

| Filename | Quant type | File Size | Description |

| -------- | ---------- | --------- | ----------- |

| dolphin-2.9-llama3-8b-Q2_K.gguf | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |

| dolphin-2.9-llama3-8b-Q3_K_S.gguf | Q3_K_S | 3.413 GB | very small, high quality loss |

| dolphin-2.9-llama3-8b-Q3_K_M.gguf | Q3_K_M | 3.743 GB | very small, high quality loss |

| dolphin-2.9-llama3-8b-Q3_K_L.gguf | Q3_K_L | 4.025 GB | small, substantial quality loss |

| dolphin-2.9-llama3-8b-Q4_0.gguf | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |

| dolphin-2.9-llama3-8b-Q4_K_S.gguf | Q4_K_S | 4.370 GB | small, greater quality loss |

| dolphin-2.9-llama3-8b-Q4_K_M.gguf | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |

| dolphin-2.9-llama3-8b-Q5_0.gguf | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |

| dolphin-2.9-llama3-8b-Q5_K_S.gguf | Q5_K_S | 5.215 GB | large, low quality loss - recommended |

| dolphin-2.9-llama3-8b-Q5_K_M.gguf | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |

| dolphin-2.9-llama3-8b-Q6_K.gguf | Q6_K | 6.143 GB | very large, extremely low quality loss |

| dolphin-2.9-llama3-8b-Q8_0.gguf | Q8_0 | 7.954 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/dolphin-2.9-llama3-8b-GGUF --include "dolphin-2.9-llama3-8b-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., Q4_Kgguf), you can try:

huggingface-cli download tensorblock/dolphin-2.9-llama3-8b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'