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codemateai/CodeMate-v0.1 - GGUF
This repo contains GGUF format model files for codemateai/CodeMate-v0.1.
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 |
---|---|---|---|
CodeMate-v0.1-Q2_K.gguf | Q2_K | 12.506 GB | smallest, significant quality loss - not recommended for most purposes |
CodeMate-v0.1-Q3_K_S.gguf | Q3_K_S | 14.605 GB | very small, high quality loss |
CodeMate-v0.1-Q3_K_M.gguf | Q3_K_M | 16.306 GB | very small, high quality loss |
CodeMate-v0.1-Q3_K_L.gguf | Q3_K_L | 17.772 GB | small, substantial quality loss |
CodeMate-v0.1-Q4_0.gguf | Q4_0 | 19.052 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
CodeMate-v0.1-Q4_K_S.gguf | Q4_K_S | 19.192 GB | small, greater quality loss |
CodeMate-v0.1-Q4_K_M.gguf | Q4_K_M | 20.220 GB | medium, balanced quality - recommended |
CodeMate-v0.1-Q5_0.gguf | Q5_0 | 23.237 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
CodeMate-v0.1-Q5_K_S.gguf | Q5_K_S | 23.237 GB | large, low quality loss - recommended |
CodeMate-v0.1-Q5_K_M.gguf | Q5_K_M | 23.839 GB | large, very low quality loss - recommended |
CodeMate-v0.1-Q6_K.gguf | Q6_K | 27.684 GB | very large, extremely low quality loss |
CodeMate-v0.1-Q8_0.gguf | Q8_0 | 35.856 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/CodeMate-v0.1-GGUF --include "CodeMate-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/CodeMate-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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Model tree for tensorblock/CodeMate-v0.1-GGUF
Base model
codemateai/CodeMate-v0.1Evaluation results
- pass@1 on HumanEvalself-reported74.9%
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard55.550
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard78.030
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard55.310
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard48.640
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard72.610
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard40.180