You can deploy this model and start earning money today!

tensorblock/
CHIH-HUNG_llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-GGUF

Name: CHIH-HUNG_llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-GGUF
Availability: InStock

CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down

Views

No views yet

Website

Discord

GitHub

CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down - GGUF

This repo contains GGUF format model files for CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

Our projects

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio
MCP Servers	Studio
A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

Prompt template

Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.

Model file specification

Filename	Quant type	File Size	Description
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q2_K.gguf	Q2_K	4.854 GB	smallest, significant quality loss - not recommended for most purposes
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q3_K_S.gguf	Q3_K_S	5.659 GB	very small, high quality loss
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q3_K_M.gguf	Q3_K_M	6.338 GB	very small, high quality loss
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q3_K_L.gguf	Q3_K_L	6.930 GB	small, substantial quality loss
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q4_0.gguf	Q4_0	7.366 GB	legacy; small, very high quality loss - prefer using Q3_K_M
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q4_K_S.gguf	Q4_K_S	7.423 GB	small, greater quality loss
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q4_K_M.gguf	Q4_K_M	7.866 GB	medium, balanced quality - recommended
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q5_0.gguf	Q5_0	8.972 GB	legacy; medium, balanced quality - prefer using Q4_K_M
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q5_K_S.gguf	Q5_K_S	8.972 GB	large, low quality loss - recommended
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q5_K_M.gguf	Q5_K_M	9.230 GB	large, very low quality loss - recommended
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q6_K.gguf	Q6_K	10.679 GB	very large, extremely low quality loss
llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-Q8_0.gguf	Q8_0	13.831 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/CHIH-HUNG_llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-GGUF --include "llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-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/CHIH-HUNG_llama-2-13b-FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'