metadata
library_name: transformers
license: apache-2.0
base_model: tanliboy/lambda-qwen2.5-14b-dpo-test
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: lambda-qwen2.5-14b-dpo-test
results: []
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tanliboy/lambda-qwen2.5-14b-dpo-test - GGUF
This repo contains GGUF format model files for tanliboy/lambda-qwen2.5-14b-dpo-test.
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 |
|---|---|---|---|
| lambda-qwen2.5-14b-dpo-test-Q2_K.gguf | Q2_K | 5.374 GB | smallest, significant quality loss - not recommended for most purposes |
| lambda-qwen2.5-14b-dpo-test-Q3_K_S.gguf | Q3_K_S | 6.202 GB | very small, high quality loss |
| lambda-qwen2.5-14b-dpo-test-Q3_K_M.gguf | Q3_K_M | 6.835 GB | very small, high quality loss |
| lambda-qwen2.5-14b-dpo-test-Q3_K_L.gguf | Q3_K_L | 7.381 GB | small, substantial quality loss |
| lambda-qwen2.5-14b-dpo-test-Q4_0.gguf | Q4_0 | 7.933 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| lambda-qwen2.5-14b-dpo-test-Q4_K_S.gguf | Q4_K_S | 7.985 GB | small, greater quality loss |
| lambda-qwen2.5-14b-dpo-test-Q4_K_M.gguf | Q4_K_M | 8.371 GB | medium, balanced quality - recommended |
| lambda-qwen2.5-14b-dpo-test-Q5_0.gguf | Q5_0 | 9.561 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| lambda-qwen2.5-14b-dpo-test-Q5_K_S.gguf | Q5_K_S | 9.561 GB | large, low quality loss - recommended |
| lambda-qwen2.5-14b-dpo-test-Q5_K_M.gguf | Q5_K_M | 9.787 GB | large, very low quality loss - recommended |
| lambda-qwen2.5-14b-dpo-test-Q6_K.gguf | Q6_K | 11.292 GB | very large, extremely low quality loss |
| lambda-qwen2.5-14b-dpo-test-Q8_0.gguf | Q8_0 | 14.623 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/lambda-qwen2.5-14b-dpo-test-GGUF --include "lambda-qwen2.5-14b-dpo-test-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/lambda-qwen2.5-14b-dpo-test-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'