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ibm-granite/granite-3b-code-instruct-2k - GGUF
This repo contains GGUF format model files for ibm-granite/granite-3b-code-instruct-2k.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
System:
{system_prompt}
Question:
{prompt}
Answer:
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
granite-3b-code-instruct-2k-Q2_K.gguf | Q2_K | 1.247 GB | smallest, significant quality loss - not recommended for most purposes |
granite-3b-code-instruct-2k-Q3_K_S.gguf | Q3_K_S | 1.445 GB | very small, high quality loss |
granite-3b-code-instruct-2k-Q3_K_M.gguf | Q3_K_M | 1.608 GB | very small, high quality loss |
granite-3b-code-instruct-2k-Q3_K_L.gguf | Q3_K_L | 1.747 GB | small, substantial quality loss |
granite-3b-code-instruct-2k-Q4_0.gguf | Q4_0 | 1.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
granite-3b-code-instruct-2k-Q4_K_S.gguf | Q4_K_S | 1.875 GB | small, greater quality loss |
granite-3b-code-instruct-2k-Q4_K_M.gguf | Q4_K_M | 1.986 GB | medium, balanced quality - recommended |
granite-3b-code-instruct-2k-Q5_0.gguf | Q5_0 | 2.251 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
granite-3b-code-instruct-2k-Q5_K_S.gguf | Q5_K_S | 2.251 GB | large, low quality loss - recommended |
granite-3b-code-instruct-2k-Q5_K_M.gguf | Q5_K_M | 2.316 GB | large, very low quality loss - recommended |
granite-3b-code-instruct-2k-Q6_K.gguf | Q6_K | 2.666 GB | very large, extremely low quality loss |
granite-3b-code-instruct-2k-Q8_0.gguf | Q8_0 | 3.451 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/granite-3b-code-instruct-2k-GGUF --include "granite-3b-code-instruct-2k-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/granite-3b-code-instruct-2k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
- 211
Model tree for tensorblock/granite-3b-code-instruct-2k-GGUF
Base model
ibm-granite/granite-3b-code-base-2k
Finetuned
ibm-granite/granite-3b-code-instruct-2k
Datasets used to train tensorblock/granite-3b-code-instruct-2k-GGUF
Evaluation results
- pass@1 on HumanEvalSynthesis(Python)self-reported51.200
- pass@1 on HumanEvalSynthesis(Python)self-reported43.900
- pass@1 on HumanEvalSynthesis(Python)self-reported41.500
- pass@1 on HumanEvalSynthesis(Python)self-reported31.700
- pass@1 on HumanEvalSynthesis(Python)self-reported40.200
- pass@1 on HumanEvalSynthesis(Python)self-reported29.300
- pass@1 on HumanEvalSynthesis(Python)self-reported39.600
- pass@1 on HumanEvalSynthesis(Python)self-reported26.800
- pass@1 on HumanEvalSynthesis(Python)self-reported39.000
- pass@1 on HumanEvalSynthesis(Python)self-reported14.000