ServiceNow-AI/Apriel-Nemotron-15b-Thinker - GGUF
This repo contains GGUF format model files for ServiceNow-AI/Apriel-Nemotron-15b-Thinker.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.
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Prompt template
<|system|>
You are a thoughtful and systematic AI assistant built by ServiceNow Language Models (SLAM) lab. Before providing an answer, analyze the problem carefully and present your reasoning step by step. After explaining your thought process, provide the final solution in the following format: [BEGIN FINAL RESPONSE] ... [END FINAL RESPONSE].
{system_prompt}
<|end|>
<|user|>
{prompt}
<|end|>
<|assistant|>
Here are my reasoning steps:
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Apriel-Nemotron-15b-Thinker-Q2_K.gguf | Q2_K | 5.794 GB | smallest, significant quality loss - not recommended for most purposes |
| Apriel-Nemotron-15b-Thinker-Q3_K_S.gguf | Q3_K_S | 6.706 GB | very small, high quality loss |
| Apriel-Nemotron-15b-Thinker-Q3_K_M.gguf | Q3_K_M | 7.396 GB | very small, high quality loss |
| Apriel-Nemotron-15b-Thinker-Q3_K_L.gguf | Q3_K_L | 7.990 GB | small, substantial quality loss |
| Apriel-Nemotron-15b-Thinker-Q4_0.gguf | Q4_0 | 8.606 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Apriel-Nemotron-15b-Thinker-Q4_K_S.gguf | Q4_K_S | 8.663 GB | small, greater quality loss |
| Apriel-Nemotron-15b-Thinker-Q4_K_M.gguf | Q4_K_M | 9.113 GB | medium, balanced quality - recommended |
| Apriel-Nemotron-15b-Thinker-Q5_0.gguf | Q5_0 | 10.393 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Apriel-Nemotron-15b-Thinker-Q5_K_S.gguf | Q5_K_S | 10.393 GB | large, low quality loss - recommended |
| Apriel-Nemotron-15b-Thinker-Q5_K_M.gguf | Q5_K_M | 10.655 GB | large, very low quality loss - recommended |
| Apriel-Nemotron-15b-Thinker-Q6_K.gguf | Q6_K | 12.293 GB | very large, extremely low quality loss |
| Apriel-Nemotron-15b-Thinker-Q8_0.gguf | Q8_0 | 15.919 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/ServiceNow-AI_Apriel-Nemotron-15b-Thinker-GGUF --include "Apriel-Nemotron-15b-Thinker-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/ServiceNow-AI_Apriel-Nemotron-15b-Thinker-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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ServiceNow-AI/Apriel-Nemotron-15b-Thinker