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Sharathhebbar24/SSH_355M - GGUF
This repo contains GGUF format model files for Sharathhebbar24/SSH_355M.
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 |
---|---|---|---|
SSH_355M-Q2_K.gguf | Q2_K | 0.178 GB | smallest, significant quality loss - not recommended for most purposes |
SSH_355M-Q3_K_S.gguf | Q3_K_S | 0.201 GB | very small, high quality loss |
SSH_355M-Q3_K_M.gguf | Q3_K_M | 0.229 GB | very small, high quality loss |
SSH_355M-Q3_K_L.gguf | Q3_K_L | 0.244 GB | small, substantial quality loss |
SSH_355M-Q4_0.gguf | Q4_0 | 0.248 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
SSH_355M-Q4_K_S.gguf | Q4_K_S | 0.250 GB | small, greater quality loss |
SSH_355M-Q4_K_M.gguf | Q4_K_M | 0.271 GB | medium, balanced quality - recommended |
SSH_355M-Q5_0.gguf | Q5_0 | 0.292 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
SSH_355M-Q5_K_S.gguf | Q5_K_S | 0.292 GB | large, low quality loss - recommended |
SSH_355M-Q5_K_M.gguf | Q5_K_M | 0.309 GB | large, very low quality loss - recommended |
SSH_355M-Q6_K.gguf | Q6_K | 0.339 GB | very large, extremely low quality loss |
SSH_355M-Q8_0.gguf | Q8_0 | 0.437 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/SSH_355M-GGUF --include "SSH_355M-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/SSH_355M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
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Model tree for tensorblock/SSH_355M-GGUF
Base model
Sharathhebbar24/SSH_355MDatasets used to train tensorblock/SSH_355M-GGUF
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard28.240
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard38.740
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard27.030
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard42.510
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard53.670
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.300