YuE-s1-7B-anneal-en-cot Open-source Large Language Model - Free Deployment and Support for Local Inference

Yue S1 7B Anneal En Cot GGUF

Developed by tensorblock

YuE-s1-7B-anneal-en-cot is a large language model with 7B parameters, providing a quantized version in GGUF format, suitable for local inference.

Large Language Model English#English chain-of-thought reasoning #7B parameter quantization #Multi-precision adaptation

Downloads 272

Release Time : 1/28/2025

Model Overview

This model is a quantized large language model mainly used for English text generation and reasoning tasks, supporting chain-of-thought (CoT) reasoning.

Model Features

GGUF format support

Provides GGUF format model files with multiple quantization levels for easy local deployment and inference.

Chain-of-thought reasoning

Supports chain-of-thought (CoT) reasoning, suitable for tasks requiring multi-step reasoning.

Multiple quantization levels

Provides multiple quantization levels from Q2_K to Q8_0 to meet the needs of different scenarios.

Model Capabilities

Text generation

Chain-of-thought reasoning

English text processing

Use Cases

Text generation

Content creation

Generate articles, stories, or other creative text content.

Reasoning tasks

Logical reasoning

Solve logical problems requiring multi-step reasoning.

🚀 m-a-p/YuE-s1-7B-anneal-en-cot - GGUF

This repository offers GGUF format model files for m-a-p/YuE-s1-7B-anneal-en-cot. These files are quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit ec7f3ac.

🚀 Quick Start

The following sections provide detailed information about the project, including other projects, prompt templates, model file specifications, and downloading instructions.

✨ Features

Our projects

Project	Description	Image	Link
Awesome MCP Servers	A comprehensive collection of Model Context Protocol (MCP) servers.		See what we built
TensorBlock Studio	A lightweight, open, and extensible multi-LLM interaction studio.		See what we built

Prompt template

Model file specification

Property	Details
Filename	YuE-s1-7B-anneal-en-cot-Q2_K.gguf, YuE-s1-7B-anneal-en-cot-Q3_K_S.gguf, YuE-s1-7B-anneal-en-cot-Q3_K_M.gguf, YuE-s1-7B-anneal-en-cot-Q3_K_L.gguf, YuE-s1-7B-anneal-en-cot-Q4_0.gguf, YuE-s1-7B-anneal-en-cot-Q4_K_S.gguf, YuE-s1-7B-anneal-en-cot-Q4_K_M.gguf, YuE-s1-7B-anneal-en-cot-Q5_0.gguf, YuE-s1-7B-anneal-en-cot-Q5_K_S.gguf, YuE-s1-7B-anneal-en-cot-Q5_K_M.gguf, YuE-s1-7B-anneal-en-cot-Q6_K.gguf, YuE-s1-7B-anneal-en-cot-Q8_0.gguf
Quant type	Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, Q4_0, Q4_K_S, Q4_K_M, Q5_0, Q5_K_S, Q5_K_M, Q6_K, Q8_0
File Size	2.432 GB, 2.812 GB, 3.096 GB, 3.340 GB, 3.593 GB, 3.617 GB, 3.788 GB, 4.328 GB, 4.328 GB, 4.429 GB, 5.109 GB, 6.617 GB
Description	smallest, significant quality loss - not recommended for most purposes; very small, high quality loss; very small, high quality loss; small, substantial quality loss; legacy; small, very high quality loss - prefer using Q3_K_M; small, greater quality loss; medium, balanced quality - recommended; legacy; medium, balanced quality - prefer using Q4_K_M; large, low quality loss - recommended; large, very low quality loss - recommended; very large, extremely low quality loss; very large, extremely low quality loss - not recommended

📦 Installation

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, download the individual model file to a local directory

huggingface-cli download tensorblock/YuE-s1-7B-anneal-en-cot-GGUF --include "YuE-s1-7B-anneal-en-cot-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you want to download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/YuE-s1-7B-anneal-en-cot-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

📄 License

This project is licensed under the CC BY-NC 4.0 license.

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