metadata
license: apache-2.0
tags:
- moe
- merge
- epfl-llm/meditron-7b
- medalpaca/medalpaca-7b
- chaoyi-wu/PMC_LLAMA_7B_10_epoch
- allenai/tulu-2-dpo-7b
- TensorBlock
- GGUF
base_model: Technoculture/Medtulu-4x7B
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
Technoculture/Medtulu-4x7B - GGUF
This repo contains GGUF format model files for Technoculture/Medtulu-4x7B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Our projects
| Awesome MCP Servers | TensorBlock Studio |
|---|---|
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| 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 👀 |
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Medtulu-4x7B-Q2_K.gguf | Q2_K | 7.235 GB | smallest, significant quality loss - not recommended for most purposes |
| Medtulu-4x7B-Q3_K_S.gguf | Q3_K_S | 8.530 GB | very small, high quality loss |
| Medtulu-4x7B-Q3_K_M.gguf | Q3_K_M | 9.489 GB | very small, high quality loss |
| Medtulu-4x7B-Q3_K_L.gguf | Q3_K_L | 10.295 GB | small, substantial quality loss |
| Medtulu-4x7B-Q4_0.gguf | Q4_0 | 11.132 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Medtulu-4x7B-Q4_K_S.gguf | Q4_K_S | 11.231 GB | small, greater quality loss |
| Medtulu-4x7B-Q4_K_M.gguf | Q4_K_M | 11.945 GB | medium, balanced quality - recommended |
| Medtulu-4x7B-Q5_0.gguf | Q5_0 | 13.581 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Medtulu-4x7B-Q5_K_S.gguf | Q5_K_S | 13.581 GB | large, low quality loss - recommended |
| Medtulu-4x7B-Q5_K_M.gguf | Q5_K_M | 14.000 GB | large, very low quality loss - recommended |
| Medtulu-4x7B-Q6_K.gguf | Q6_K | 16.184 GB | very large, extremely low quality loss |
| Medtulu-4x7B-Q8_0.gguf | Q8_0 | 20.960 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/Medtulu-4x7B-GGUF --include "Medtulu-4x7B-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/Medtulu-4x7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'