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Model: prithivMLmods/Qwen3-0.6B-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- moe
+---
+
+# **Qwen3-0.6B-GGUF**
+
+>  Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support
+
+## Model Files
+
+| File Name                | Size    | Quantization | Format | Description                      |
+| ------------------------ | ------- | ------------ | ------ | -------------------------------- |
+| `Qwen3_0.6B.F32.gguf`    | 2.39 GB | FP32         | GGUF   | Full precision (float32) version |
+| `Qwen3_0.6B.BF16.gguf`   | 1.2 GB  | BF16         | GGUF   | BFloat16 precision version       |
+| `Qwen3_0.6B.F16.gguf`    | 1.2 GB  | FP16         | GGUF   | Float16 precision version        |
+| `Qwen3_0.6B.Q3_K_M.gguf` | 347 MB  | Q3\_K\_M     | GGUF   | 3-bit quantized (K M variant)    |
+| `Qwen3_0.6B.Q3_K_S.gguf` | 323 MB  | Q3\_K\_S     | GGUF   | 3-bit quantized (K S variant)    |
+| `Qwen3_0.6B.Q4_K_M.gguf` | 397 MB  | Q4\_K\_M     | GGUF   | 4-bit quantized (K M variant)    |
+| `Qwen3_0.6B.Q4_K_S.gguf` | 383 MB  | Q4\_K\_S     | GGUF   | 4-bit quantized (K S variant)    |
+| `Qwen3_0.6B.Q5_K_M.gguf` | 444 MB  | Q5\_K\_M     | GGUF   | 5-bit quantized (K M variant)    |
+| `Qwen3_0.6B.Q8_0.gguf`   | 639 MB  | Q8\_0        | GGUF   | 8-bit quantized                  |
+| `.gitattributes`         | 2.04 kB | —            | —      | Git LFS tracking file            |
+| `config.json`            | 31 B    | —            | —      | Configuration placeholder        |
+| `README.md`              | 3.53 kB | —            | —      | Model documentation              |
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) | Q2_K | 0.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) | Q6_K | 0.7 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) | f16 | 1.6 | 16 bpw, overkill |
+
+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}