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jrz_summarized_QwQcln8k_QwQNoisySm8k_dscln8k-DSCln8k_QWQCln8k_QWQNoisySm8k_Qwen-7B_Instruct_sft – AI Model by secmlr | AlphaNeural AI
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secmlr
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jrz_summarized_QwQcln8k_QwQNoisySm8k_dscln8k-DSCln8k_QWQCln8k_QWQNoisySm8k_Qwen-7B_Instruct_sft
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0
transformers
safetensors
qwen2
text-generation
llama-factory
full
generated_from_trainer
conversational
secmlr/VD-DS-Clean-8k_VD-QWQ-Clean-8k_VD-QWQ-Noisy-Small-8k_Qwen2.5-7B-Instruct_full_sft_1e-5
finetune
apache-2.0
autotrain_compatible
text-generation-inference
endpoints_compatible
us
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jrz_summarized_QwQcln8k_QwQNoisySm8k_dscln8k-DSCln8k_QWQCln8k_QWQNoisySm8k_Qwen-7B_Instruct_sft
This model is a fine-tuned version of
secmlr/VD-DS-Clean-8k_VD-QWQ-Clean-8k_VD-QWQ-Noisy-Small-8k_Qwen2.5-7B-Instruct_full_sft_1e-5
on the ruizhe_simplier_reasoning, the ruizhe_simplier_reasoning_QwQ_noisy_small8k and the ruizhe_simplier_reasoning_ds8k datasets.
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: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 12
total_train_batch_size: 48
total_eval_batch_size: 32
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: 0.1
num_epochs: 3.0
Training results
Framework versions
Transformers 4.46.1
Pytorch 2.6.0+cu124
Datasets 3.1.0
Tokenizers 0.20.3
