This model is a fine-tuned version of
meta-llama/Llama-3.2-3B-Instruct
on the minpeter/xlam-function-calling-60k-hermes, the minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes, the minpeter/hermes-function-calling-v1-jsonl and the minpeter/hermes-function-calling-v1-jsonl datasets.
It achieves the following results on the evaluation set:
Loss: 0.3335
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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_8BIT 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: 10
num_epochs: 2.0
Training results
Training Loss
Epoch
Step
Validation Loss
0.5354
0.0039
1
0.7727
0.4667
0.3327
85
0.3745
0.1858
0.6654
170
0.3515
0.5982
0.9980
255
0.3440
0.1452
1.3288
340
0.3389
0.2287
1.6614
425
0.3344
0.1441
1.9941
510
0.3335
Framework versions
PEFT 0.14.0
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0
Runs of minpeter LoRA-Llama-3b-xlam-vllm-test-ci on huggingface.co
49
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
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