metadata
license: mit
language:
- en
- kn
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- bilingual
- kannada
- english
- TensorBlock
- GGUF
base_model: fierysurf/Ambari-7B-Instruct-v0.1-sharded
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
fierysurf/Ambari-7B-Instruct-v0.1-sharded - GGUF
This repo contains GGUF format model files for fierysurf/Ambari-7B-Instruct-v0.1-sharded.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Prompt template
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Ambari-7B-Instruct-v0.1-sharded-Q2_K.gguf | Q2_K | 2.616 GB | smallest, significant quality loss - not recommended for most purposes |
| Ambari-7B-Instruct-v0.1-sharded-Q3_K_S.gguf | Q3_K_S | 3.039 GB | very small, high quality loss |
| Ambari-7B-Instruct-v0.1-sharded-Q3_K_M.gguf | Q3_K_M | 3.389 GB | very small, high quality loss |
| Ambari-7B-Instruct-v0.1-sharded-Q3_K_L.gguf | Q3_K_L | 3.688 GB | small, substantial quality loss |
| Ambari-7B-Instruct-v0.1-sharded-Q4_0.gguf | Q4_0 | 3.926 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Ambari-7B-Instruct-v0.1-sharded-Q4_K_S.gguf | Q4_K_S | 3.957 GB | small, greater quality loss |
| Ambari-7B-Instruct-v0.1-sharded-Q4_K_M.gguf | Q4_K_M | 4.181 GB | medium, balanced quality - recommended |
| Ambari-7B-Instruct-v0.1-sharded-Q5_0.gguf | Q5_0 | 4.761 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Ambari-7B-Instruct-v0.1-sharded-Q5_K_S.gguf | Q5_K_S | 4.761 GB | large, low quality loss - recommended |
| Ambari-7B-Instruct-v0.1-sharded-Q5_K_M.gguf | Q5_K_M | 4.892 GB | large, very low quality loss - recommended |
| Ambari-7B-Instruct-v0.1-sharded-Q6_K.gguf | Q6_K | 5.648 GB | very large, extremely low quality loss |
| Ambari-7B-Instruct-v0.1-sharded-Q8_0.gguf | Q8_0 | 7.315 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/Ambari-7B-Instruct-v0.1-sharded-GGUF --include "Ambari-7B-Instruct-v0.1-sharded-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/Ambari-7B-Instruct-v0.1-sharded-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'