metadata
license: apache-2.0
pipeline_tag: text-generation
language:
- en
- he
tags:
- pretrained
- TensorBlock
- GGUF
inference:
parameters:
temperature: 0.7
base_model: dicta-il/dictalm2.0
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
dicta-il/dictalm2.0 - GGUF
This repo contains GGUF format model files for dicta-il/dictalm2.0.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| dictalm2.0-Q2_K.gguf | Q2_K | 2.538 GB | smallest, significant quality loss - not recommended for most purposes |
| dictalm2.0-Q3_K_S.gguf | Q3_K_S | 2.953 GB | very small, high quality loss |
| dictalm2.0-Q3_K_M.gguf | Q3_K_M | 3.283 GB | very small, high quality loss |
| dictalm2.0-Q3_K_L.gguf | Q3_K_L | 3.565 GB | small, substantial quality loss |
| dictalm2.0-Q4_0.gguf | Q4_0 | 3.833 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| dictalm2.0-Q4_K_S.gguf | Q4_K_S | 3.862 GB | small, greater quality loss |
| dictalm2.0-Q4_K_M.gguf | Q4_K_M | 4.075 GB | medium, balanced quality - recommended |
| dictalm2.0-Q5_0.gguf | Q5_0 | 4.661 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| dictalm2.0-Q5_K_S.gguf | Q5_K_S | 4.661 GB | large, low quality loss - recommended |
| dictalm2.0-Q5_K_M.gguf | Q5_K_M | 4.786 GB | large, very low quality loss - recommended |
| dictalm2.0-Q6_K.gguf | Q6_K | 5.541 GB | very large, extremely low quality loss |
| dictalm2.0-Q8_0.gguf | Q8_0 | 7.177 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/dictalm2.0-GGUF --include "dictalm2.0-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/dictalm2.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'