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JT-Math-8B-Thinking-GGUF
benjamin920101/JT-Math-8B-Thinking-GGUF
JT-Math-8B-Thinking-GGUF is a machine learning model from benjamin920101. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This repository contains GGUF format model files converted from JT-LM/JT-Math-8B-Thinking, optimized for llama.cpp and other GGUF-compatible inference clients (such as LM Studio, Ollama, AnythingLLM, etc.).
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At a glance
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- a5ca5d41
Model card
From the Hugging Face model README
JT-LM/JT-Math-8B-Thinking-GGUF
This repository contains GGUF format model files converted from JT-LM/JT-Math-8B-Thinking, optimized for llama.cpp and other GGUF-compatible inference clients (such as LM Studio, Ollama, AnythingLLM, etc.).
Model Overview
JT-Math-8B-Thinking is an 8-billion parameter open-source Large Language Model designed specifically for advanced mathematical reasoning and complex problem-solving. Fine-tuned on high-quality bilingual (Chinese and English) datasets, the model features strong long-context processing capabilities and powerful Chain-of-Thought (CoT) reasoning.
- Key Features:
- Long Context Support: Natively supports up to a 32,768 (32K) context window.
- Deep Reasoning: Optimized via multi-stage Reinforcement Learning (RL) and curriculum learning, making it exceptionally good at generating deep reasoning paths to solve competition-level math problems.
- Bilingual Optimization: Delivers top-tier mathematical derivation performance in both Chinese and English environments.
File List & Quantization Options
This repository offers two high-precision versions, ideal for scenarios that demand ultimate reasoning quality and have sufficient hardware resources:
| File Name | Type | File Size | Recommended RAM/VRAM | Description |
|---|---|---|---|---|
JT-Math-8B-Thinking-Q8_0.gguf | Q8_0 Quantization | ~8.5 GB | >= 12 GB | Recommended Choice. Almost lossless 8-bit quantization that perfectly balances inference speed and model performance. Suitable for most modern CPUs and GPUs. |
JT-Math-8B-Thinking-F16.gguf | F16 Native | ~16.1 GB | >= 24 GB | Lossless Version. Retains the original Float16 precision. Ideal for resource-rich environments (e.g., 24GB VRAM GPUs) where any quantization loss is unacceptable. |
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026