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RichardErkhov/lightblue_-_reranker_0.5_cont_filt-gguf
lightblue_-_reranker_0.5_cont_filt-gguf is a text ranking model from RichardErkhov. Use it for the text ranking task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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.gguf10.5 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
reranker_0.5_cont_filt - GGUF
| Name | Quant method | Size |
|---|---|---|
| reranker_0.5_cont_filt.Q2_K.gguf | Q2_K | 0.39GB |
| reranker_0.5_cont_filt.IQ3_XS.gguf | IQ3_XS | 0.39GB |
| reranker_0.5_cont_filt.IQ3_S.gguf | IQ3_S | 0.39GB |
| reranker_0.5_cont_filt.Q3_K_S.gguf | Q3_K_S | 0.39GB |
| reranker_0.5_cont_filt.IQ3_M.gguf | IQ3_M | 0.39GB |
| reranker_0.5_cont_filt.Q3_K.gguf | Q3_K | 0.4GB |
| reranker_0.5_cont_filt.Q3_K_M.gguf | Q3_K_M | 0.4GB |
| reranker_0.5_cont_filt.Q3_K_L.gguf | Q3_K_L | 0.42GB |
| reranker_0.5_cont_filt.IQ4_XS.gguf | IQ4_XS | 0.4GB |
| reranker_0.5_cont_filt.Q4_0.gguf | Q4_0 | 0.4GB |
| reranker_0.5_cont_filt.IQ4_NL.gguf | IQ4_NL | 0.4GB |
| reranker_0.5_cont_filt.Q4_K_S.gguf | Q4_K_S | 0.45GB |
| reranker_0.5_cont_filt.Q4_K.gguf | Q4_K | 0.46GB |
| reranker_0.5_cont_filt.Q4_K_M.gguf | Q4_K_M | 0.46GB |
| reranker_0.5_cont_filt.Q4_1.gguf | Q4_1 | 0.43GB |
| reranker_0.5_cont_filt.Q5_0.gguf | Q5_0 | 0.46GB |
| reranker_0.5_cont_filt.Q5_K_S.gguf | Q5_K_S | 0.48GB |
| reranker_0.5_cont_filt.Q5_K.gguf | Q5_K | 0.49GB |
| reranker_0.5_cont_filt.Q5_K_M.gguf | Q5_K_M | 0.49GB |
| reranker_0.5_cont_filt.Q5_1.gguf | Q5_1 | 0.49GB |
| reranker_0.5_cont_filt.Q6_K.gguf | Q6_K | 0.61GB |
| reranker_0.5_cont_filt.Q8_0.gguf | Q8_0 | 0.63GB |
library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags:
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the reranker_continuous_filt_train dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2895 | 0.1000 | 2016 | 0.3479 |
| 0.2891 | 0.2000 | 4032 | 0.3320 |
| 0.396 | 0.3000 | 6048 | 0.3245 |
| 0.2693 | 0.4000 | 8064 | 0.3080 |
| 0.2712 | 0.5000 | 10080 | 0.3056 |
| 0.2738 | 0.6000 | 12096 | 0.2925 |
| 0.1629 | 0.7000 | 14112 | 0.2880 |
| 0.2761 | 0.8000 | 16128 | 0.2839 |
| 0.1861 | 0.9000 | 18144 | 0.2813 |