metadata
license: mit
datasets:
- trendmicro-ailab/Primus-Reasoning
- trendmicro-ailab/Primus-Seed
- trendmicro-ailab/Primus-FineWeb
- trendmicro-ailab/Primus-Instruct
language:
- en
base_model: trendmicro-ailab/Llama-Primus-Reasoning
pipeline_tag: text-generation
library_name: transformers
tags:
- cybersecurity
- pretraining
- TensorBlock
- GGUF
extra_gated_fields:
Affiliation: text
Country: country
I want to use this model for:
type: select
options:
- Research
- Commercial
- label: Other
value: other
Job title:
type: select
options:
- Student
- Research graduate
- AI researcher
- AI developer/engineer
- Cybersecurity researcher
- Reporter
- Other
geo: ip_location
trendmicro-ailab/Llama-Primus-Reasoning - GGUF
This repo contains GGUF format model files for trendmicro-ailab/Llama-Primus-Reasoning.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.
Our projects
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Prompt template
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Llama-Primus-Reasoning-Q2_K.gguf | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| Llama-Primus-Reasoning-Q3_K_S.gguf | Q3_K_S | 3.665 GB | very small, high quality loss |
| Llama-Primus-Reasoning-Q3_K_M.gguf | Q3_K_M | 4.019 GB | very small, high quality loss |
| Llama-Primus-Reasoning-Q3_K_L.gguf | Q3_K_L | 4.322 GB | small, substantial quality loss |
| Llama-Primus-Reasoning-Q4_0.gguf | Q4_0 | 4.661 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Llama-Primus-Reasoning-Q4_K_S.gguf | Q4_K_S | 4.693 GB | small, greater quality loss |
| Llama-Primus-Reasoning-Q4_K_M.gguf | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| Llama-Primus-Reasoning-Q5_0.gguf | Q5_0 | 5.599 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Llama-Primus-Reasoning-Q5_K_S.gguf | Q5_K_S | 5.599 GB | large, low quality loss - recommended |
| Llama-Primus-Reasoning-Q5_K_M.gguf | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| Llama-Primus-Reasoning-Q6_K.gguf | Q6_K | 6.596 GB | very large, extremely low quality loss |
| Llama-Primus-Reasoning-Q8_0.gguf | Q8_0 | 8.541 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/trendmicro-ailab_Llama-Primus-Reasoning-GGUF --include "Llama-Primus-Reasoning-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/trendmicro-ailab_Llama-Primus-Reasoning-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'