pipeline_tag, inference, license, datasets, metrics, library_name, tags, base_model, model-index

pipeline_tag	inference	license	datasets	metrics	library_name	tags	base_model	model-index
text-generation	false	apache-2.0	bigcode/commitpackft TIGER-Lab/MathInstruct meta-math/MetaMathQA glaiveai/glaive-code-assistant-v3 glaive-function-calling-v2 bugdaryan/sql-create-context-instruction garage-bAInd/Open-Platypus nvidia/HelpSteer bigcode/self-oss-instruct-sc2-exec-filter-50k	code_eval	transformers	code granite TensorBlock GGUF	ibm-granite/granite-8b-code-instruct-128k	name results granite-8B-Code-instruct-128k task dataset metrics type text-generation name type HumanEvalSynthesis (Python) bigcode/humanevalpack type value name verified pass@1 62.2 pass@1 false type value name verified pass@1 51.4 pass@1 false type value name verified pass@1 38.9 pass@1 false type value name verified pass@1 38.3 pass@1 false task dataset metrics type text-generation name type RepoQA (Python@16K) repoqa type value name verified pass@1 (thresh=0.5) 73.0 pass@1 (thresh=0.5) false type value name verified pass@1 (thresh=0.5) 37.0 pass@1 (thresh=0.5) false type value name verified pass@1 (thresh=0.5) 73.0 pass@1 (thresh=0.5) false type value name verified pass@1 (thresh=0.5) 62.0 pass@1 (thresh=0.5) false type value name verified pass@1 (thresh=0.5) 63.0 pass@1 (thresh=0.5) false

ibm-granite/granite-8b-code-instruct-128k - GGUF

This repo contains GGUF format model files for ibm-granite/granite-8b-code-instruct-128k.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

System:
{system_prompt}

Question:
{prompt}

Answer:

Model file specification

Filename	Quant type	File Size	Description
granite-8b-code-instruct-128k-Q2_K.gguf	Q2_K	2.852 GB	smallest, significant quality loss - not recommended for most purposes
granite-8b-code-instruct-128k-Q3_K_S.gguf	Q3_K_S	3.304 GB	very small, high quality loss
granite-8b-code-instruct-128k-Q3_K_M.gguf	Q3_K_M	3.674 GB	very small, high quality loss
granite-8b-code-instruct-128k-Q3_K_L.gguf	Q3_K_L	3.993 GB	small, substantial quality loss
granite-8b-code-instruct-128k-Q4_0.gguf	Q4_0	4.276 GB	legacy; small, very high quality loss - prefer using Q3_K_M
granite-8b-code-instruct-128k-Q4_K_S.gguf	Q4_K_S	4.305 GB	small, greater quality loss
granite-8b-code-instruct-128k-Q4_K_M.gguf	Q4_K_M	4.548 GB	medium, balanced quality - recommended
granite-8b-code-instruct-128k-Q5_0.gguf	Q5_0	5.190 GB	legacy; medium, balanced quality - prefer using Q4_K_M
granite-8b-code-instruct-128k-Q5_K_S.gguf	Q5_K_S	5.190 GB	large, low quality loss - recommended
granite-8b-code-instruct-128k-Q5_K_M.gguf	Q5_K_M	5.330 GB	large, very low quality loss - recommended
granite-8b-code-instruct-128k-Q6_K.gguf	Q6_K	6.161 GB	very large, extremely low quality loss
granite-8b-code-instruct-128k-Q8_0.gguf	Q8_0	7.977 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-8b-code-instruct-128k-GGUF --include "granite-8b-code-instruct-128k-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-8b-code-instruct-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'