# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KitsuVp/NeoLLM", trust_remote_code=True, dtype="auto")Quick Links
NeoLLM
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.3300
- eval_runtime: 88.5321
- eval_samples_per_second: 170.277
- eval_steps_per_second: 2.666
- epoch: 0.96
- step: 45000
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: 0.0006
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 1
Framework versions
- Transformers 5.8.1
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KitsuVp/NeoLLM", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)