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RichardErkhov/
lightblue_-_reranker_0.5_cont-gguf

Name: lightblue_-_reranker_0.5_cont-gguf
Availability: InStock

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reranker_0.5_cont - GGUF

Model creator: https://huggingface.co/lightblue/
Original model: https://huggingface.co/lightblue/reranker_0.5_cont/

Name	Quant method	Size
reranker_0.5_cont.Q2_K.gguf	Q2_K	0.39GB
reranker_0.5_cont.IQ3_XS.gguf	IQ3_XS	0.39GB
reranker_0.5_cont.IQ3_S.gguf	IQ3_S	0.39GB
reranker_0.5_cont.Q3_K_S.gguf	Q3_K_S	0.39GB
reranker_0.5_cont.IQ3_M.gguf	IQ3_M	0.39GB
reranker_0.5_cont.Q3_K.gguf	Q3_K	0.4GB
reranker_0.5_cont.Q3_K_M.gguf	Q3_K_M	0.4GB
reranker_0.5_cont.Q3_K_L.gguf	Q3_K_L	0.42GB
reranker_0.5_cont.IQ4_XS.gguf	IQ4_XS	0.4GB
reranker_0.5_cont.Q4_0.gguf	Q4_0	0.4GB
reranker_0.5_cont.IQ4_NL.gguf	IQ4_NL	0.4GB
reranker_0.5_cont.Q4_K_S.gguf	Q4_K_S	0.45GB
reranker_0.5_cont.Q4_K.gguf	Q4_K	0.46GB
reranker_0.5_cont.Q4_K_M.gguf	Q4_K_M	0.46GB
reranker_0.5_cont.Q4_1.gguf	Q4_1	0.43GB
reranker_0.5_cont.Q5_0.gguf	Q5_0	0.46GB
reranker_0.5_cont.Q5_K_S.gguf	Q5_K_S	0.48GB
reranker_0.5_cont.Q5_K.gguf	Q5_K	0.49GB
reranker_0.5_cont.Q5_K_M.gguf	Q5_K_M	0.49GB
reranker_0.5_cont.Q5_1.gguf	Q5_1	0.49GB
reranker_0.5_cont.Q6_K.gguf	Q6_K	0.61GB
reranker_0.5_cont.Q8_0.gguf	Q8_0	0.63GB

Original model description:

library_name: transformers license: other base_model: Qwen/Qwen2.5-0.5B-Instruct tags:

llama-factory
full
generated_from_trainer model-index:
name: reranker_continuous_train results: []

reranker_continuous_train

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the reranker_continuous_train dataset. It achieves the following results on the evaluation set:

Loss: 0.3195

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4242	0.1	2156	0.3844
0.236	0.2	4312	0.3643
0.6602	0.3	6468	0.3521
0.3464	0.4	8624	0.3472
0.3598	0.5	10780	0.3412
0.3377	0.6	12936	0.3341
0.4547	0.7	15092	0.3258
0.2282	0.8	17248	0.3228
0.2692	0.9	19404	0.3195
0.2059	1.0	21560	0.3195

Framework versions

Transformers 4.46.1
Pytorch 2.4.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3

RichardErkhov/lightblue_-_reranker_0.5_cont-gguf

Original model description:

reranker_continuous_train

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
lightblue_-_reranker_0.5_cont-gguf