--- language: - en - it library_name: transformers base_model: DeepMount00/Llama-3.1-8b-Ita tags: - TensorBlock - GGUF model-index: - name: Llama-3.1-8b-ITA results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 79.17 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 30.93 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 10.88 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 5.03 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 11.4 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 31.96 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard ---
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## DeepMount00/Llama-3.1-8b-Ita - GGUF This repo contains GGUF format model files for [DeepMount00/Llama-3.1-8b-Ita](https://huggingface.co/DeepMount00/Llama-3.1-8b-Ita). 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). ## Prompt template ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Llama-3.1-8b-Ita-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes | | [Llama-3.1-8b-Ita-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss | | [Llama-3.1-8b-Ita-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss | | [Llama-3.1-8b-Ita-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss | | [Llama-3.1-8b-Ita-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Llama-3.1-8b-Ita-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss | | [Llama-3.1-8b-Ita-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended | | [Llama-3.1-8b-Ita-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Llama-3.1-8b-Ita-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended | | [Llama-3.1-8b-Ita-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended | | [Llama-3.1-8b-Ita-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss | | [Llama-3.1-8b-Ita-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-3.1-8b-Ita-GGUF/tree/main/Llama-3.1-8b-Ita-Q8_0.gguf) | Q8_0 | 7.954 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/Llama-3.1-8b-Ita-GGUF --include "Llama-3.1-8b-Ita-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/Llama-3.1-8b-Ita-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```