FluentlyLM-Prinum-GGUF Open-source Large Language Model - Supports Multilingual Text and Code Generation Tasks

Fluentlylm Prinum GGUF

Developed by tensorblock

FluentlyLM-Prinum is a large language model supporting multiple languages, suitable for text generation tasks and supporting the processing of multiple languages and codes.

Large Language Model

Transformers

Supports Multiple LanguagesOpen Source License:MIT #Multilingual text generation #Mathematical reasoning enhancement #Zero-shot instruction following

Downloads 152

Release Time : 3/6/2025

Model Overview

FluentlyLM-Prinum is a large language model based on the GGUF format, supporting multiple languages such as English, French, Spanish, Russian, Chinese, Japanese, and Persian, and is suitable for a wide range of text generation tasks.

Model Features

Multilingual support

Supports multiple languages such as English, French, Spanish, Russian, Chinese, Japanese, and Persian, suitable for international application scenarios.

Wide range of application scenarios

Suitable for text generation tasks, with evaluation results on multiple datasets, including IFEval, BBH, MATH Lvl 5, etc.

Compatible with GGUF format

The model file uses the GGUF format and is compatible with llama.cpp, facilitating deployment and use.

Model Capabilities

Text generation

Multilingual processing

Code processing

Use Cases

Text generation

Multilingual text generation

Generate text content in multiple languages, suitable for international applications.

Code generation

Generate code snippets to assist in development work.

Education

Mathematical problem solving

Solve mathematical problems, suitable for educational auxiliary tools.

The exact match rate on the MATH Lvl 5 dataset is 54.0.

🚀 fluently-lm/FluentlyLM-Prinum - GGUF

This repository stores GGUF format model files for fluently-lm/FluentlyLM-Prinum. These files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4823.

✨ Features

Supported Languages: en, fr, es, ru, zh, ja, fa, code
License: MIT
Library Name: transformers
Tags: fluently-lm, fluently, prinum, instruct, trained, math, roleplay, reasoning, axolotl, unsloth, argilla, qwen2, TensorBlock, GGUF
Datasets: fluently-sets/ultraset, fluently-sets/ultrathink, fluently-sets/reasoning-1-1k, fluently-sets/MATH-500-Overall
Inference: true
Pipeline Tag: text-generation
Base Model: fluently-lm/FluentlyLM-Prinum

Model Performance Results

Task	Dataset	Metric	Value	Source
Text Generation	IFEval (0-Shot)	strict accuracy	80.9	Open LLM Leaderboard
Text Generation	BBH (3-Shot)	normalized accuracy	59.48	Open LLM Leaderboard
Text Generation	MATH Lvl 5 (4-Shot)	exact match	54.0	Open LLM Leaderboard
Text Generation	GPQA (0-shot)	acc_norm	18.23	Open LLM Leaderboard
Text Generation	MuSR (0-shot)	acc_norm	17.26	Open LLM Leaderboard
Text Generation	MMLU-PRO (5-shot)	accuracy	53.42	Open LLM Leaderboard

📦 Our Projects

Forge

Description: An OpenAI-compatible multi-provider routing layer.
Preview:
Try it: 🚀 Try it now! 🚀

Awesome MCP Servers

Description: A comprehensive collection of Model Context Protocol (MCP) servers.
Preview:
Explore: 👀 See what we built 👀

TensorBlock Studio

Description: A lightweight, open, and extensible multi-LLM interaction studio.
Preview:
Explore: 👀 See what we built 👀

💻 Prompt Template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

📚 Model File Specification

Filename	Quant Type	File Size	Description
FluentlyLM-Prinum-Q2_K.gguf	Q2_K	12.313 GB	smallest, significant quality loss - not recommended for most purposes
FluentlyLM-Prinum-Q3_K_S.gguf	Q3_K_S	14.392 GB	very small, high quality loss
FluentlyLM-Prinum-Q3_K_M.gguf	Q3_K_M	15.935 GB	very small, high quality loss
FluentlyLM-Prinum-Q3_K_L.gguf	Q3_K_L	17.247 GB	small, substantial quality loss
FluentlyLM-Prinum-Q4_0.gguf	Q4_0	18.640 GB	legacy; small, very high quality loss - prefer using Q3_K_M
FluentlyLM-Prinum-Q4_K_S.gguf	Q4_K_S	18.784 GB	small, greater quality loss
FluentlyLM-Prinum-Q4_K_M.gguf	Q4_K_M	19.851 GB	medium, balanced quality - recommended
FluentlyLM-Prinum-Q5_0.gguf	Q5_0	22.638 GB	legacy; medium, balanced quality - prefer using Q4_K_M
FluentlyLM-Prinum-Q5_K_S.gguf	Q5_K_S	22.638 GB	large, low quality loss - recommended
FluentlyLM-Prinum-Q5_K_M.gguf	Q5_K_M	23.262 GB	large, very low quality loss - recommended
FluentlyLM-Prinum-Q6_K.gguf	Q6_K	26.886 GB	very large, extremely low quality loss
FluentlyLM-Prinum-Q8_0.gguf	Q8_0	34.821 GB	very large, extremely low quality loss - not recommended

📦 Downloading Instruction

Command line

Step 1: Install Huggingface Client

pip install -U "huggingface_hub[cli]"

Step 2: Download a single model file

huggingface-cli download tensorblock/FluentlyLM-Prinum-GGUF --include "FluentlyLM-Prinum-Q2_K.gguf" --local-dir MY_LOCAL_DIR

Step 3: Download multiple model files with a pattern

huggingface-cli download tensorblock/FluentlyLM-Prinum-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

📄 License

This project is licensed under the MIT license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご