初始化项目，由ModelHub XC社区提供模型

Model: Nodmix/Nodmix-Q3
Source: Original Platform

README.md

@@ -0,0 +1,42 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Nodmix
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Nodmix
+- agent
+- code
+---
+
+# **Nodmix-Q4**
+
+>  Nodmix is the latest generation of large language models in Nodmix IQ series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Nodmix delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support
+
+## Model Files
+
+| File Name              | Size    | Quantization | Format | Description                      |
+| ---------------------- | ------- | ------------ | ------ | -------------------------------- |
+| `Nodmix_Q4.F32.gguf`    | 16.1 GB | FP32         | GGUF   | Full precision (float32) version |
+| `Nodmix_4B.BF16.gguf`   | 8.05 GB | BF16         | GGUF   | BFloat16 precision version       |
+| `Nodmix_4B.F16.gguf`    | 8.05 GB | FP16         | GGUF   | Float16 precision version        |
+| `Nodmix_4B.Q3_K_M.gguf` | 2.08 GB | Q3\_K\_M     | GGUF   | 3-bit quantized (K M variant)    |
+| `Nodmix_4B.Q3_K_S.gguf` | 1.89 GB | Q3\_K\_S     | GGUF   | 3-bit quantized (K S variant)    |
+| `Nodmix_4B.Q4_K_M.gguf` | 2.5 GB  | Q4\_K\_M     | GGUF   | 4-bit quantized (K M variant)    |
+| `Nodmix_4B.Q4_K_S.gguf` | 2.38 GB | Q4\_K\_S     | GGUF   | 4-bit quantized (K S variant)    |
+| `Nodmix_4B.Q5_K_M.gguf` | 2.89 GB | Q5\_K\_M     | GGUF   | 5-bit quantized (K M variant)    |
+| `Nodmix_4B.Q8_0.gguf`   | 4.28 GB | Q8\_0        | GGUF   | 8-bit quantized                  |
+| `.gitattributes`       | 2.02 kB | —            | —      | Git LFS tracking file            |
+| `config.json`          | 31 B    | —            | —      | Configuration placeholder        |
+| `README.md`            | 3.6 kB  | —            | —      | Model documentation              |
+
+## 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):