初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/II-Search-CIR-4B-GGUF
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- Intelligent-Internet/II-Search-CIR-4B
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+
+# **II-Search-CIR-4B-GGUF**
+
+> II-Search-CIR-4B is a 4-billion-parameter language model built on Qwen3-4B and enhanced with Code-Integrated Reasoning (CIR), enabling it not only to call external tools (such as web search and web visit) through code blocks during inference, but also to programmatically process, filter, and reason over results within those code blocks; optimized through supervised fine-tuning and reinforcement learning on challenging reasoning datasets, the model achieves state-of-the-art or leading results on major factual QA and information-seeking benchmarks (like OpenAI/SimpleQA, Google/Frames, and Seal_0), and it can be efficiently deployed using vLLM or SGLang with up to 128k-token contexts (with YaRN RoPE scaling), supporting advanced research, educational, and web-integrated applications, with datasets, code samples, and evaluation results provided in the official Hugging Face repository.
+
+# Model Files
+
+| File Name | Size | Quant Type |
+|-----------|------|------------|
+| II-Search-4B-GGUF.BF16.gguf | 8.05 GB | BF16 |
+| II-Search-4B-GGUF.F16.gguf | 8.05 GB | F16 |
+| II-Search-4B-GGUF.F32.gguf | 16.1 GB | F32 |
+| II-Search-4B-GGUF.Q2_K.gguf | 1.67 GB | Q2_K |
+| II-Search-4B-GGUF.Q3_K_L.gguf | 2.24 GB | Q3_K_L |
+| II-Search-4B-GGUF.Q3_K_M.gguf | 2.08 GB | Q3_K_M |
+| II-Search-4B-GGUF.Q3_K_S.gguf | 1.89 GB | Q3_K_S |
+| II-Search-4B-GGUF.Q4_K_M.gguf | 2.5 GB | Q4_K_M |
+| II-Search-4B-GGUF.Q4_K_S.gguf | 2.38 GB | Q4_K_S |
+| II-Search-4B-GGUF.Q5_K_M.gguf | 2.89 GB | Q5_K_M |
+| II-Search-4B-GGUF.Q5_K_S.gguf | 2.82 GB | Q5_K_S |
+| II-Search-4B-GGUF.Q6_K.gguf | 3.31 GB | Q6_K |
+| II-Search-4B-GGUF.Q8_0.gguf | 4.28 GB | Q8_0 |
+
+## 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)