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Model: prithivMLmods/Light-IF-4B-f32-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model:
+- qihoo360/Light-IF-4B
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+
+# **Light-IF-4B-f32-GGUF**
+
+> Light-IF-4B is a 4-billion-parameter instruction-following language model derived from Qwen3-4B-Base, designed to overcome "lazy reasoning" in complex tasks by incorporating previewing and self-checking during inference; it is fine-tuned using entropy-preserving supervised learning (Entropy-SFT) and token-wise entropy-adaptive reinforcement learning (TEA-RL) on a carefully filtered dataset, producing strong results across instruction-following and reasoning benchmarks (such as SuperClue, IFEval, and IFBench), where it matches or outperforms even larger or closed-source models, and supports advanced features such as extended context (32k-131k tokens with YaRN), efficient deployment (via Hugging Face Transformers, sglang, or vllm), and open integration for research in robust generalizable reasoning, with further details, evaluation code, and licensing on its official Hugging Face repository and paper.
+
+# Model Files
+
+| File Name | Size | Quant Type |
+|-----------|------|------------|
+| Qwen3-4B-MegaScience.BF16.gguf | 8.05 GB | BF16 |
+| Qwen3-4B-MegaScience.F16.gguf | 8.05 GB | F16 |
+| Qwen3-4B-MegaScience.F32.gguf | 16.1 GB | F32 |
+| Qwen3-4B-MegaScience.Q3_K_L.gguf | 2.24 GB | Q3_K_L |
+| Qwen3-4B-MegaScience.Q3_K_S.gguf | 1.89 GB | Q3_K_S |
+| Qwen3-4B-MegaScience.Q4_K_M.gguf | 2.5 GB | Q4_K_M |
+| Qwen3-4B-MegaScience.Q4_K_S.gguf | 2.38 GB | Q4_K_S |
+| Qwen3-4B-MegaScience.Q5_K_M.gguf | 2.89 GB | Q5_K_M |
+| Qwen3-4B-MegaScience.Q5_K_S.gguf | 2.82 GB | Q5_K_S |
+| Qwen3-4B-MegaScience.Q6_K.gguf | 3.31 GB | Q6_K |
+| Qwen3-4B-MegaScience.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)

config.json

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

configuration.json

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+{"framework": "pytorch", "task": "others", "allow_remote": true}