metadata
library_name: transformers
license: other
base_model: yiran-wang3/ds_chat_reflection_adamw_iter1
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- TensorBlock
- GGUF
datasets:
- >-
self-generate/iter_exp_reflct_ds_chat_regular_original_cn_mining_oj_iter0-binarized-reflection-scored
model-index:
- name: ds_chat_reflection_adamw_iter1
results: []
yiran-wang3/ds_chat_reflection_adamw_iter1 - GGUF
This repo contains GGUF format model files for yiran-wang3/ds_chat_reflection_adamw_iter1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| ds_chat_reflection_adamw_iter1-Q2_K.gguf | Q2_K | 2.718 GB | smallest, significant quality loss - not recommended for most purposes |
| ds_chat_reflection_adamw_iter1-Q3_K_S.gguf | Q3_K_S | 3.138 GB | very small, high quality loss |
| ds_chat_reflection_adamw_iter1-Q3_K_M.gguf | Q3_K_M | 3.461 GB | very small, high quality loss |
| ds_chat_reflection_adamw_iter1-Q3_K_L.gguf | Q3_K_L | 3.746 GB | small, substantial quality loss |
| ds_chat_reflection_adamw_iter1-Q4_0.gguf | Q4_0 | 4.000 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| ds_chat_reflection_adamw_iter1-Q4_K_S.gguf | Q4_K_S | 4.025 GB | small, greater quality loss |
| ds_chat_reflection_adamw_iter1-Q4_K_M.gguf | Q4_K_M | 4.223 GB | medium, balanced quality - recommended |
| ds_chat_reflection_adamw_iter1-Q5_0.gguf | Q5_0 | 4.811 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| ds_chat_reflection_adamw_iter1-Q5_K_S.gguf | Q5_K_S | 4.811 GB | large, low quality loss - recommended |
| ds_chat_reflection_adamw_iter1-Q5_K_M.gguf | Q5_K_M | 4.926 GB | large, very low quality loss - recommended |
| ds_chat_reflection_adamw_iter1-Q6_K.gguf | Q6_K | 5.673 GB | very large, extremely low quality loss |
| ds_chat_reflection_adamw_iter1-Q8_0.gguf | Q8_0 | 7.347 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/ds_chat_reflection_adamw_iter1-GGUF --include "ds_chat_reflection_adamw_iter1-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/ds_chat_reflection_adamw_iter1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'