metadata
library_name: transformers
tags:
- medical
- gemma2
- TensorBlock
- GGUF
license: apache-2.0
datasets:
- lavita/ChatDoctor-HealthCareMagic-100k
language:
- en
pipeline_tag: question-answering
base_model: kingabzpro/Gemma-2-9b-it-chat-doctor
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
kingabzpro/Gemma-2-9b-it-chat-doctor - GGUF
This repo contains GGUF format model files for kingabzpro/Gemma-2-9b-it-chat-doctor.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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 |
|---|---|---|---|
| Gemma-2-9b-it-chat-doctor-Q2_K.gguf | Q2_K | 3.805 GB | smallest, significant quality loss - not recommended for most purposes |
| Gemma-2-9b-it-chat-doctor-Q3_K_S.gguf | Q3_K_S | 4.338 GB | very small, high quality loss |
| Gemma-2-9b-it-chat-doctor-Q3_K_M.gguf | Q3_K_M | 4.762 GB | very small, high quality loss |
| Gemma-2-9b-it-chat-doctor-Q3_K_L.gguf | Q3_K_L | 5.132 GB | small, substantial quality loss |
| Gemma-2-9b-it-chat-doctor-Q4_0.gguf | Q4_0 | 5.443 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Gemma-2-9b-it-chat-doctor-Q4_K_S.gguf | Q4_K_S | 5.479 GB | small, greater quality loss |
| Gemma-2-9b-it-chat-doctor-Q4_K_M.gguf | Q4_K_M | 5.761 GB | medium, balanced quality - recommended |
| Gemma-2-9b-it-chat-doctor-Q5_0.gguf | Q5_0 | 6.484 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Gemma-2-9b-it-chat-doctor-Q5_K_S.gguf | Q5_K_S | 6.484 GB | large, low quality loss - recommended |
| Gemma-2-9b-it-chat-doctor-Q5_K_M.gguf | Q5_K_M | 6.647 GB | large, very low quality loss - recommended |
| Gemma-2-9b-it-chat-doctor-Q6_K.gguf | Q6_K | 7.589 GB | very large, extremely low quality loss |
| Gemma-2-9b-it-chat-doctor-Q8_0.gguf | Q8_0 | 9.827 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/Gemma-2-9b-it-chat-doctor-GGUF --include "Gemma-2-9b-it-chat-doctor-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/Gemma-2-9b-it-chat-doctor-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'