BEE-spoke-data/zephyr-220m-sft-full-GGUF

Quantized GGUF model files for zephyr-220m-sft-full from BEE-spoke-data

Name	Quant method	Size
zephyr-220m-sft-full.fp16.gguf	fp16	436.50 MB
zephyr-220m-sft-full.q2_k.gguf	q2_k	94.43 MB
zephyr-220m-sft-full.q3_k_m.gguf	q3_k_m	114.65 MB
zephyr-220m-sft-full.q4_k_m.gguf	q4_k_m	137.58 MB
zephyr-220m-sft-full.q5_k_m.gguf	q5_k_m	157.91 MB
zephyr-220m-sft-full.q6_k.gguf	q6_k	179.52 MB
zephyr-220m-sft-full.q8_0.gguf	q8_0	232.28 MB

Original Model Card:

zephyr-220m-sft-full

This model is a fine-tuned version of BEE-spoke-data/smol_llama-220M-openhermes on the Ultrachat_200k dataset. It achieves the following results on the evaluation set:

• Loss: 1.6579

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: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.6447	1.0	1624	1.6579

Framework versions

• Transformers 4.37.0.dev0
• Pytorch 2.1.2+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0

https://wandb.ai/amazingvince/huggingface/runs/5rffzk3x/workspace?workspace=user-amazingvince