BEE-spoke-data/smol_llama-101M-midjourney-messages-GGUF
Quantized GGUF model files for smol_llama-101M-midjourney-messages from BEE-spoke-data
| Name | Quant method | Size |
|---|---|---|
| smol_llama-101m-midjourney-messages.fp16.gguf | fp16 | 203.28 MB |
| smol_llama-101m-midjourney-messages.q2_k.gguf | q2_k | 50.93 MB |
| smol_llama-101m-midjourney-messages.q3_k_m.gguf | q3_k_m | 57.06 MB |
| smol_llama-101m-midjourney-messages.q4_k_m.gguf | q4_k_m | 65.40 MB |
| smol_llama-101m-midjourney-messages.q5_k_m.gguf | q5_k_m | 74.34 MB |
| smol_llama-101m-midjourney-messages.q6_k.gguf | q6_k | 83.83 MB |
| smol_llama-101m-midjourney-messages.q8_0.gguf | q8_0 | 108.35 MB |
Original Model Card:
smol_llama-101M-midjourney-messages
Given a 'partial prompt' for a text2image model, this generates additional relevant text to include for a full prompt.
Model description
This model is a fine-tuned version of BEE-spoke-data/smol_llama-101M-GQA on the pszemraj/midjourney-messages-cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8431
- Accuracy: 0.4682
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 4
- eval_batch_size: 4
- seed: 17056
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0