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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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- merge |
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- mergekit |
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- TensorBlock |
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- GGUF |
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base_model: shadowml/Mixolar-4x7b |
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model-index: |
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- name: Mixolar-4x7b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 71.08 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 88.44 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 66.29 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 71.81 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 83.58 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 63.91 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/Mixolar-4x7b |
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name: Open LLM Leaderboard |
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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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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> |
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</p> |
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</div> |
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</div> |
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## shadowml/Mixolar-4x7b - GGUF |
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This repo contains GGUF format model files for [shadowml/Mixolar-4x7b](https://huggingface.co/shadowml/Mixolar-4x7b). |
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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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## Prompt template |
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``` |
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### System: |
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{system_prompt} |
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### User: |
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{prompt} |
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### Assistant: |
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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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| [Mixolar-4x7b-Q2_K.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q2_K.gguf) | Q2_K | 12.283 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [Mixolar-4x7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_S.gguf) | Q3_K_S | 14.499 GB | very small, high quality loss | |
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| [Mixolar-4x7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_M.gguf) | Q3_K_M | 16.101 GB | very small, high quality loss | |
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| [Mixolar-4x7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q3_K_L.gguf) | Q3_K_L | 17.447 GB | small, substantial quality loss | |
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| [Mixolar-4x7b-Q4_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_0.gguf) | Q4_0 | 18.947 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [Mixolar-4x7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_K_S.gguf) | Q4_K_S | 19.113 GB | small, greater quality loss | |
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| [Mixolar-4x7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q4_K_M.gguf) | Q4_K_M | 20.325 GB | medium, balanced quality - recommended | |
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| [Mixolar-4x7b-Q5_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_0.gguf) | Q5_0 | 23.134 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [Mixolar-4x7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_K_S.gguf) | Q5_K_S | 23.134 GB | large, low quality loss - recommended | |
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| [Mixolar-4x7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q5_K_M.gguf) | Q5_K_M | 23.844 GB | large, very low quality loss - recommended | |
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| [Mixolar-4x7b-Q6_K.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q6_K.gguf) | Q6_K | 27.583 GB | very large, extremely low quality loss | |
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| [Mixolar-4x7b-Q8_0.gguf](https://huggingface.co/tensorblock/Mixolar-4x7b-GGUF/tree/main/Mixolar-4x7b-Q8_0.gguf) | Q8_0 | 35.725 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/Mixolar-4x7b-GGUF --include "Mixolar-4x7b-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/Mixolar-4x7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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