Hugging Face's logo

tensorblock
/
Aira-2-774M-GGUF

Text Generation
Transformers
GGUF
English
alignment
instruction tuned
text generation
conversation
assistant
TensorBlock
GGUF
Model card Files Community
Aira-2-774M-GGUF / README.md
morriszms's picture
morriszms
Update README.md
04e1ac6 verified
preview code
|
raw
history blame contribute delete
7.16 kB
metadata
license: apache-2.0
datasets:
  - nicholasKluge/instruct-aira-dataset
language:
  - en
metrics:
  - accuracy
library_name: transformers
tags:
  - alignment
  - instruction tuned
  - text generation
  - conversation
  - assistant
  - TensorBlock
  - GGUF
pipeline_tag: text-generation
widget:
  - text: >-
      <|startofinstruction|>Can you explain what is Machine
      Learning?<|endofinstruction|>
    example_title: Machine Learning
  - text: >-
      <|startofinstruction|>Do you know anything about virtue
      ethics?<|endofinstruction|>
    example_title: Ethics
  - text: >-
      <|startofinstruction|>How can I make my girlfriend
      happy?<|endofinstruction|>
    example_title: Advise
inference:
  parameters:
    repetition_penalty: 1.2
    temperature: 0.2
    top_k: 30
    top_p: 0.3
    max_new_tokens: 200
    length_penalty: 0.3
    early_stopping: true
co2_eq_emissions:
  emissions: 770
  source: CodeCarbon
  training_type: fine-tuning
  geographical_location: United States of America
  hardware_used: NVIDIA A100-SXM4-40GB
base_model: nicholasKluge/Aira-2-774M
TensorBlock

Website Twitter Discord GitHub Telegram

nicholasKluge/Aira-2-774M - GGUF

This repo contains GGUF format model files for nicholasKluge/Aira-2-774M.

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

Our projects

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers TensorBlock Studio
MCP Servers Studio
A comprehensive collection of Model Context Protocol (MCP) servers. A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀 👀 See what we built 👀
## Prompt template

Model file specification

Filename Quant type File Size Description
Aira-2-774M-Q2_K.gguf Q2_K 0.322 GB smallest, significant quality loss - not recommended for most purposes
Aira-2-774M-Q3_K_S.gguf Q3_K_S 0.367 GB very small, high quality loss
Aira-2-774M-Q3_K_M.gguf Q3_K_M 0.427 GB very small, high quality loss
Aira-2-774M-Q3_K_L.gguf Q3_K_L 0.460 GB small, substantial quality loss
Aira-2-774M-Q4_0.gguf Q4_0 0.462 GB legacy; small, very high quality loss - prefer using Q3_K_M
Aira-2-774M-Q4_K_S.gguf Q4_K_S 0.465 GB small, greater quality loss
Aira-2-774M-Q4_K_M.gguf Q4_K_M 0.511 GB medium, balanced quality - recommended
Aira-2-774M-Q5_0.gguf Q5_0 0.552 GB legacy; medium, balanced quality - prefer using Q4_K_M
Aira-2-774M-Q5_K_S.gguf Q5_K_S 0.552 GB large, low quality loss - recommended
Aira-2-774M-Q5_K_M.gguf Q5_K_M 0.589 GB large, very low quality loss - recommended
Aira-2-774M-Q6_K.gguf Q6_K 0.648 GB very large, extremely low quality loss
Aira-2-774M-Q8_0.gguf Q8_0 0.836 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/Aira-2-774M-GGUF --include "Aira-2-774M-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/Aira-2-774M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'