r1-1776-distill-llama-70b-GGUF

tensorblock/r1-1776-distill-llama-70b-GGUF

r1-1776-distill-llama-70b-GGUF is a machine learning model from tensorblock. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

<div style="width: auto; margin-left: auto; margin-right: auto" <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;" </div

Downloads · 30 days

1% of all-time downloads

All-time downloads

4.2K

Public

Repo size

532 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.gguf26.4 GB · 100%

At a glance

License: mit
Access: Public
Created: Feb 21, 2025
Updated: Jan 27, 2026
SHA: 47fe5a02

Base models

perplexity-ai/r1-1776-distill-llama-70b

Model card

From the Hugging Face model README

perplexity-ai/r1-1776-distill-llama-70b - GGUF

This repo contains GGUF format model files for perplexity-ai/r1-1776-distill-llama-70b.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4658.

Our projects

<table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template

<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜>

Model file specification

Filename	Quant type	File Size	Description
r1-1776-distill-llama-70b-Q2_K.gguf	Q2_K	26.375 GB	smallest, significant quality loss - not recommended for most purposes
r1-1776-distill-llama-70b-Q3_K_S.gguf	Q3_K_S	30.912 GB	very small, high quality loss
r1-1776-distill-llama-70b-Q3_K_M.gguf	Q3_K_M	34.267 GB	very small, high quality loss
r1-1776-distill-llama-70b-Q3_K_L.gguf	Q3_K_L	37.141 GB	small, substantial quality loss
r1-1776-distill-llama-70b-Q4_0.gguf	Q4_0	39.970 GB	legacy; small, very high quality loss - prefer using Q3_K_M
r1-1776-distill-llama-70b-Q4_K_S.gguf	Q4_K_S	40.347 GB	small, greater quality loss
r1-1776-distill-llama-70b-Q4_K_M.gguf	Q4_K_M	42.520 GB	medium, balanced quality - recommended
r1-1776-distill-llama-70b-Q5_0.gguf	Q5_0	48.657 GB	legacy; medium, balanced quality - prefer using Q4_K_M
r1-1776-distill-llama-70b-Q5_K_S.gguf	Q5_K_S	48.657 GB	large, low quality loss - recommended
r1-1776-distill-llama-70b-Q5_K_M.gguf	Q5_K_M	49.950 GB	large, very low quality loss - recommended
r1-1776-distill-llama-70b-Q6_K	Q6_K	57.888 GB	very large, extremely low quality loss
r1-1776-distill-llama-70b-Q8_0	Q8_0	74.975 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/r1-1776-distill-llama-70b-GGUF --include "r1-1776-distill-llama-70b-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/r1-1776-distill-llama-70b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

License: mit
Created: Feb 21, 2025
Updated: Jan 27, 2026