--- language: - en license: apache-2.0 datasets: - ehartford/dolphin - jondurbin/airoboros-2.2.1 - ehartford/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - LDJnr/Capybara - argilla/ultrafeedback-binarized-preferences-cleaned tags: - TensorBlock - GGUF base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo model-index: - name: dolphin-2.6-mistral-7b-dpo results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 65.61 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 61.47 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 48.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cognitivecomputations/dolphin-2.6-mistral-7b-dpo name: Open LLM Leaderboard ---
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## cognitivecomputations/dolphin-2.6-mistral-7b-dpo - GGUF This repo contains GGUF format model files for [cognitivecomputations/dolphin-2.6-mistral-7b-dpo](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [dolphin-2.6-mistral-7b-dpo-Q2_K.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q2_K.gguf) | Q2_K | 2.719 GB | smallest, significant quality loss - not recommended for most purposes | | [dolphin-2.6-mistral-7b-dpo-Q3_K_S.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q3_K_S.gguf) | Q3_K_S | 3.165 GB | very small, high quality loss | | [dolphin-2.6-mistral-7b-dpo-Q3_K_M.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q3_K_M.gguf) | Q3_K_M | 3.519 GB | very small, high quality loss | | [dolphin-2.6-mistral-7b-dpo-Q3_K_L.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q3_K_L.gguf) | Q3_K_L | 3.822 GB | small, substantial quality loss | | [dolphin-2.6-mistral-7b-dpo-Q4_0.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q4_0.gguf) | Q4_0 | 4.109 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [dolphin-2.6-mistral-7b-dpo-Q4_K_S.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q4_K_S.gguf) | Q4_K_S | 4.140 GB | small, greater quality loss | | [dolphin-2.6-mistral-7b-dpo-Q4_K_M.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q4_K_M.gguf) | Q4_K_M | 4.368 GB | medium, balanced quality - recommended | | [dolphin-2.6-mistral-7b-dpo-Q5_0.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q5_0.gguf) | Q5_0 | 4.998 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [dolphin-2.6-mistral-7b-dpo-Q5_K_S.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q5_K_S.gguf) | Q5_K_S | 4.998 GB | large, low quality loss - recommended | | [dolphin-2.6-mistral-7b-dpo-Q5_K_M.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q5_K_M.gguf) | Q5_K_M | 5.131 GB | large, very low quality loss - recommended | | [dolphin-2.6-mistral-7b-dpo-Q6_K.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q6_K.gguf) | Q6_K | 5.942 GB | very large, extremely low quality loss | | [dolphin-2.6-mistral-7b-dpo-Q8_0.gguf](https://huggingface.co/tensorblock/dolphin-2.6-mistral-7b-dpo-GGUF/blob/main/dolphin-2.6-mistral-7b-dpo-Q8_0.gguf) | Q8_0 | 7.696 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/dolphin-2.6-mistral-7b-dpo-GGUF --include "dolphin-2.6-mistral-7b-dpo-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/dolphin-2.6-mistral-7b-dpo-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```