anthonymeo_-_full-train-1b-gguf – AI Model by RichardErkhov | AlphaNeural AI

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anthonymeo_-_full-train-1b-gguf

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full-train-1b - GGUF

Model creator: https://huggingface.co/anthonymeo/
Original model: https://huggingface.co/anthonymeo/full-train-1b/

Name	Quant method	Size
full-train-1b.Q2_K.gguf	Q2_K	0.54GB
full-train-1b.IQ3_XS.gguf	IQ3_XS	0.58GB
full-train-1b.IQ3_S.gguf	IQ3_S	0.6GB
full-train-1b.Q3_K_S.gguf	Q3_K_S	0.6GB
full-train-1b.IQ3_M.gguf	IQ3_M	0.61GB
full-train-1b.Q3_K.gguf	Q3_K	0.64GB
full-train-1b.Q3_K_M.gguf	Q3_K_M	0.64GB
full-train-1b.Q3_K_L.gguf	Q3_K_L	0.68GB
full-train-1b.IQ4_XS.gguf	IQ4_XS	0.7GB
full-train-1b.Q4_0.gguf	Q4_0	0.72GB
full-train-1b.IQ4_NL.gguf	IQ4_NL	0.72GB
full-train-1b.Q4_K_S.gguf	Q4_K_S	0.72GB
full-train-1b.Q4_K.gguf	Q4_K	0.75GB
full-train-1b.Q4_K_M.gguf	Q4_K_M	0.75GB
full-train-1b.Q4_1.gguf	Q4_1	0.77GB
full-train-1b.Q5_0.gguf	Q5_0	0.83GB
full-train-1b.Q5_K_S.gguf	Q5_K_S	0.83GB
full-train-1b.Q5_K.gguf	Q5_K	0.85GB
full-train-1b.Q5_K_M.gguf	Q5_K_M	0.85GB
full-train-1b.Q5_1.gguf	Q5_1	0.89GB
full-train-1b.Q6_K.gguf	Q6_K	0.95GB
full-train-1b.Q8_0.gguf	Q8_0	1.23GB

Original model description:

library_name: transformers license: other base_model: meta-llama/Llama-3.2-1B tags:

llama-factory
full
generated_from_trainer model-index:
name: sft results: []

sft

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the train dataset. It achieves the following results on the evaluation set:

Loss: 0.2643

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: 1e-05
train_batch_size: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Framework versions

Transformers 4.45.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.20.0