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Llama-3-8B-WildChat-100k-qwen2-72b-osc

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the penfever/allenai_WildChat-1M-Full-Qwen_Qwen2.5-72B-Instruct and the OpenScholar/OS_Train_Data datasets. It achieves the following results on the evaluation set:

• Loss: 0.6211

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• 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_steps: 100
• num_epochs: 1

Training results

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.21.0

Base model

meta-llama/Llama-3.1-8B

(1426)

All model checkpoints associated with the WildChat-50m paper, including Re-Wild, DPO, and TULU3. • 58 items • Updated Jan 29, 2025