auto-patch README.md

README.md

@@ -33,7 +33,7 @@ static quants of https://huggingface.co/CodeGoat24/UnifiedReward-2.0-qwen-3b
 
 ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UnifiedReward-2.0-qwen-3b-GGUF).***
 
-weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
+weighted/imatrix quants are available at https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-i1-GGUF
 ## Usage
 
 If you are unsure how to use GGUF files, refer to one of [TheBloke's
@@ -47,7 +47,19 @@ more details, including on how to concatenate multi-part files.
 | Link | Type | Size/GB | Notes |
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q2_K.gguf) | Q2_K | 1.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.mmproj-f16.gguf) | mmproj-f16 | 1.4 | multi-modal supplement |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q3_K_S.gguf) | Q3_K_S | 1.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q3_K_L.gguf) | Q3_K_L | 1.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.IQ4_XS.gguf) | IQ4_XS | 1.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q5_K_S.gguf) | Q5_K_S | 2.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q5_K_M.gguf) | Q5_K_M | 2.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/UnifiedReward-2.0-qwen-3b-GGUF/resolve/main/UnifiedReward-2.0-qwen-3b.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
 
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):