初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Jan-nano-F32-GGUF
Source: Original Platform

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Jan-nano.BF16.gguf

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Jan-nano.F16.gguf

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Jan-nano.F32.gguf

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Menlo/Jan-nano
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- MCP
+---
+
+# **Jan-nano-GGUF**
+
+> Jan-Nano is a compact 4-billion parameter language model specifically designed and trained for deep research tasks. This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers, enabling efficient integration with various research tools and data sources.
+
+## Model Files
+
+| File Name | Size | Format | Description |
+|-----------|------|--------|-------------|
+| Jan-nano.F32.gguf | 16.1 GB | F32 | Full precision 32-bit floating point |
+| Jan-nano.F16.gguf | 8.05 GB | F16 | Half precision 16-bit floating point |
+| Jan-nano.BF16.gguf | 8.05 GB | BF16 | Brain floating point 16-bit |
+
+## Usage
+
+These GGUF format files are optimized for use with llama.cpp and compatible inference engines. Choose the appropriate precision level based on your hardware capabilities and quality requirements:
+
+- **F32**: Highest quality, requires most memory
+- **F16/BF16**: Good balance of quality and memory efficiency
+
+## Configuration
+
+The model configuration is available in `config.json`.
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

config.json

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+{
+    "model_type": "qwen3"
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}