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Model: tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF Source: Original Platform
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llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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license: llama3
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base_model: ruslandev/llama-3-8b-gpt-4o-ru1.0
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tags:
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- generated_from_trainer
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- TensorBlock
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- GGUF
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datasets:
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- ruslandev/tagengo-rus-gpt-4o
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model-index:
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- name: home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru
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results: []
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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[](https://tensorblock.co)
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[](https://twitter.com/tensorblock_aoi)
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[](https://discord.gg/Ej5NmeHFf2)
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[](https://github.com/TensorBlock)
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[](https://t.me/TensorBlock)
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## ruslandev/llama-3-8b-gpt-4o-ru1.0 - GGUF
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This repo contains GGUF format model files for [ruslandev/llama-3-8b-gpt-4o-ru1.0](https://huggingface.co/ruslandev/llama-3-8b-gpt-4o-ru1.0).
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
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## Our projects
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<table border="1" cellspacing="0" cellpadding="10">
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<tr>
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<th colspan="2" style="font-size: 25px;">Forge</th>
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</tr>
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<tr>
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<th colspan="2">
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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
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</th>
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</tr>
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<tr>
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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
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</tr>
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<tr>
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<th colspan="2">
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<a href="https://github.com/TensorBlock/forge" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">🚀 Try it now! 🚀</a>
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</th>
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</tr>
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<tr>
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<th style="font-size: 25px;">Awesome MCP Servers</th>
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<th style="font-size: 25px;">TensorBlock Studio</th>
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</tr>
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<tr>
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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
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</tr>
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<tr>
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
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</tr>
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<tr>
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<th>
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<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">👀 See what we built 👀</a>
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</th>
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<th>
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<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
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display: inline-block;
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padding: 8px 16px;
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background-color: #FF7F50;
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color: white;
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text-decoration: none;
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border-radius: 6px;
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font-weight: bold;
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font-family: sans-serif;
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">👀 See what we built 👀</a>
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</th>
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</tr>
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</table>
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## Prompt template
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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## Model file specification
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
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| [llama-3-8b-gpt-4o-ru1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_S.gguf) | Q3_K_S | 3.664 GB | very small, high quality loss |
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| [llama-3-8b-gpt-4o-ru1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
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| [llama-3-8b-gpt-4o-ru1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
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| [llama-3-8b-gpt-4o-ru1.0-Q4_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_0.gguf) | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [llama-3-8b-gpt-4o-ru1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
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| [llama-3-8b-gpt-4o-ru1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
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| [llama-3-8b-gpt-4o-ru1.0-Q5_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_0.gguf) | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [llama-3-8b-gpt-4o-ru1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_K_S.gguf) | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
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| [llama-3-8b-gpt-4o-ru1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
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| [llama-3-8b-gpt-4o-ru1.0-Q6_K.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
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| [llama-3-8b-gpt-4o-ru1.0-Q8_0.gguf](https://huggingface.co/tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF/blob/main/llama-3-8b-gpt-4o-ru1.0-Q8_0.gguf) | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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### Command line
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Firstly, install Huggingface Client
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```shell
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
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```shell
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huggingface-cli download tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF --include "llama-3-8b-gpt-4o-ru1.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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```
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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```shell
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huggingface-cli download tensorblock/llama-3-8b-gpt-4o-ru1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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version https://git-lfs.github.com/spec/v1
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oid sha256:3ef06e59ca3d4f66d3c5e45e4c9dd53fb60e8395843141adef3f70166ce46e8d
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size 3179131904
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version https://git-lfs.github.com/spec/v1
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oid sha256:be325cd993fe561f130b7c970b4625c6f95290259d1679276d8c8ab895dc93b9
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size 4018918400
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