metadata
base_model: abacusai/Smaug-34B-v0.1
license: apache-2.0
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
abacusai/Smaug-34B-v0.1 - GGUF
This repo contains GGUF format model files for abacusai/Smaug-34B-v0.1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
[INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Smaug-34B-v0.1-Q2_K.gguf | Q2_K | 11.944 GB | smallest, significant quality loss - not recommended for most purposes |
| Smaug-34B-v0.1-Q3_K_S.gguf | Q3_K_S | 13.933 GB | very small, high quality loss |
| Smaug-34B-v0.1-Q3_K_M.gguf | Q3_K_M | 15.511 GB | very small, high quality loss |
| Smaug-34B-v0.1-Q3_K_L.gguf | Q3_K_L | 16.894 GB | small, substantial quality loss |
| Smaug-34B-v0.1-Q4_0.gguf | Q4_0 | 18.130 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Smaug-34B-v0.1-Q4_K_S.gguf | Q4_K_S | 18.253 GB | small, greater quality loss |
| Smaug-34B-v0.1-Q4_K_M.gguf | Q4_K_M | 19.240 GB | medium, balanced quality - recommended |
| Smaug-34B-v0.1-Q5_0.gguf | Q5_0 | 22.080 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Smaug-34B-v0.1-Q5_K_S.gguf | Q5_K_S | 22.080 GB | large, low quality loss - recommended |
| Smaug-34B-v0.1-Q5_K_M.gguf | Q5_K_M | 22.651 GB | large, very low quality loss - recommended |
| Smaug-34B-v0.1-Q6_K.gguf | Q6_K | 26.276 GB | very large, extremely low quality loss |
| Smaug-34B-v0.1-Q8_0.gguf | Q8_0 | 34.033 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/Smaug-34B-v0.1-GGUF --include "Smaug-34B-v0.1-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/Smaug-34B-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'