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terry69_-_qwen_0.5_feedback_dirty-gguf – AI Model by RichardErkhov | AlphaNeural AI
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terry69_-_qwen_0.5_feedback_dirty-gguf
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Quantization made by Richard Erkhov.
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qwen_0.5_feedback_dirty - GGUF
Model creator:
https://huggingface.co/terry69/
Original model:
https://huggingface.co/terry69/qwen_0.5_feedback_dirty/
Name
Quant method
Size
qwen_0.5_feedback_dirty.Q2_K.gguf
Q2_K
0.23GB
qwen_0.5_feedback_dirty.IQ3_XS.gguf
IQ3_XS
0.24GB
qwen_0.5_feedback_dirty.IQ3_S.gguf
IQ3_S
0.25GB
qwen_0.5_feedback_dirty.Q3_K_S.gguf
Q3_K_S
0.25GB
qwen_0.5_feedback_dirty.IQ3_M.gguf
IQ3_M
0.26GB
qwen_0.5_feedback_dirty.Q3_K.gguf
Q3_K
0.26GB
qwen_0.5_feedback_dirty.Q3_K_M.gguf
Q3_K_M
0.26GB
qwen_0.5_feedback_dirty.Q3_K_L.gguf
Q3_K_L
0.28GB
qwen_0.5_feedback_dirty.IQ4_XS.gguf
IQ4_XS
0.28GB
qwen_0.5_feedback_dirty.Q4_0.gguf
Q4_0
0.29GB
qwen_0.5_feedback_dirty.IQ4_NL.gguf
IQ4_NL
0.29GB
qwen_0.5_feedback_dirty.Q4_K_S.gguf
Q4_K_S
0.29GB
qwen_0.5_feedback_dirty.Q4_K.gguf
Q4_K
0.3GB
qwen_0.5_feedback_dirty.Q4_K_M.gguf
Q4_K_M
0.3GB
qwen_0.5_feedback_dirty.Q4_1.gguf
Q4_1
0.3GB
qwen_0.5_feedback_dirty.Q5_0.gguf
Q5_0
0.32GB
qwen_0.5_feedback_dirty.Q5_K_S.gguf
Q5_K_S
0.32GB
qwen_0.5_feedback_dirty.Q5_K.gguf
Q5_K
0.33GB
qwen_0.5_feedback_dirty.Q5_K_M.gguf
Q5_K_M
0.33GB
qwen_0.5_feedback_dirty.Q5_1.gguf
Q5_1
0.34GB
qwen_0.5_feedback_dirty.Q6_K.gguf
Q6_K
0.36GB
qwen_0.5_feedback_dirty.Q8_0.gguf
Q8_0
0.47GB
Original model description:
library_name: transformers license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags:
alignment-handbook
trl
sft
generated_from_trainer
trl
sft
generated_from_trainer datasets:
preference-data model-index:
name: qwen_0.5_feedback_dirty results: []
qwen_0.5_feedback_dirty
This model is a fine-tuned version of
Qwen/Qwen1.5-0.5B-Chat
on the preference-data dataset.
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: 4
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 16
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
Training results
Framework versions
Transformers 4.45.1
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0
