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metadata
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
language:
  - en
license: apache-2.0
tags:
  - TensorBlock
  - GGUF
base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
TensorBlock

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cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser - GGUF

This repo contains GGUF format model files for cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename Quant type File Size Description
dolphin-2.6-mistral-7b-dpo-laser-Q2_K.gguf Q2_K 2.719 GB smallest, significant quality loss - not recommended for most purposes
dolphin-2.6-mistral-7b-dpo-laser-Q3_K_S.gguf Q3_K_S 3.165 GB very small, high quality loss
dolphin-2.6-mistral-7b-dpo-laser-Q3_K_M.gguf Q3_K_M 3.519 GB very small, high quality loss
dolphin-2.6-mistral-7b-dpo-laser-Q3_K_L.gguf Q3_K_L 3.822 GB small, substantial quality loss
dolphin-2.6-mistral-7b-dpo-laser-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-laser-Q4_K_S.gguf Q4_K_S 4.140 GB small, greater quality loss
dolphin-2.6-mistral-7b-dpo-laser-Q4_K_M.gguf Q4_K_M 4.368 GB medium, balanced quality - recommended
dolphin-2.6-mistral-7b-dpo-laser-Q5_0.gguf Q5_0 4.998 GB legacy; medium, balanced quality - prefer using Q4_K_M
dolphin-2.6-mistral-7b-dpo-laser-Q5_K_S.gguf Q5_K_S 4.998 GB large, low quality loss - recommended
dolphin-2.6-mistral-7b-dpo-laser-Q5_K_M.gguf Q5_K_M 5.131 GB large, very low quality loss - recommended
dolphin-2.6-mistral-7b-dpo-laser-Q6_K.gguf Q6_K 5.942 GB very large, extremely low quality loss
dolphin-2.6-mistral-7b-dpo-laser-Q8_0.gguf Q8_0 7.696 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/dolphin-2.6-mistral-7b-dpo-laser-GGUF --include "dolphin-2.6-mistral-7b-dpo-laser-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:

huggingface-cli download tensorblock/dolphin-2.6-mistral-7b-dpo-laser-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'