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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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datasets: |
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- cerebras/SlimPajama-627B |
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metrics: |
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- accuracy |
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base_model: keeeeenw/MicroLlama |
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tags: |
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- TensorBlock |
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- GGUF |
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model-index: |
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- name: MicroLlama |
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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: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 19.85 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 2.83 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 0.0 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 1.45 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 4.79 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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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: 1.53 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=keeeeenw/MicroLlama |
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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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## keeeeenw/MicroLlama - GGUF |
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This repo contains GGUF format model files for [keeeeenw/MicroLlama](https://huggingface.co/keeeeenw/MicroLlama). |
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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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<div style="text-align: left; margin: 20px 0;"> |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> |
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Run them on the TensorBlock client using your local machine ↗ |
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</a> |
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</div> |
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## Prompt template |
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``` |
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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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| [MicroLlama-Q2_K.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q2_K.gguf) | Q2_K | 0.117 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [MicroLlama-Q3_K_S.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q3_K_S.gguf) | Q3_K_S | 0.135 GB | very small, high quality loss | |
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| [MicroLlama-Q3_K_M.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q3_K_M.gguf) | Q3_K_M | 0.145 GB | very small, high quality loss | |
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| [MicroLlama-Q3_K_L.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q3_K_L.gguf) | Q3_K_L | 0.155 GB | small, substantial quality loss | |
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| [MicroLlama-Q4_0.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q4_0.gguf) | Q4_0 | 0.168 GB | legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [MicroLlama-Q4_K_S.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q4_K_S.gguf) | Q4_K_S | 0.169 GB | small, greater quality loss | |
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| [MicroLlama-Q4_K_M.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q4_K_M.gguf) | Q4_K_M | 0.177 GB | medium, balanced quality - recommended | |
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| [MicroLlama-Q5_0.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q5_0.gguf) | Q5_0 | 0.200 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [MicroLlama-Q5_K_S.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q5_K_S.gguf) | Q5_K_S | 0.200 GB | large, low quality loss - recommended | |
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| [MicroLlama-Q5_K_M.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q5_K_M.gguf) | Q5_K_M | 0.204 GB | large, very low quality loss - recommended | |
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| [MicroLlama-Q6_K.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q6_K.gguf) | Q6_K | 0.233 GB | very large, extremely low quality loss | |
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| [MicroLlama-Q8_0.gguf](https://huggingface.co/tensorblock/MicroLlama-GGUF/blob/main/MicroLlama-Q8_0.gguf) | Q8_0 | 0.302 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/MicroLlama-GGUF --include "MicroLlama-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/MicroLlama-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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