skyai798_-_safety_v2_math_v1-gguf – AI Model by RichardErkhov | AlphaNeural AI

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skyai798_-_safety_v2_math_v1-gguf

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safety_v2_math_v1 - GGUF

Model creator: https://huggingface.co/skyai798/
Original model: https://huggingface.co/skyai798/safety_v2_math_v1/

Name	Quant method	Size
safety_v2_math_v1.Q2_K.gguf	Q2_K	2.96GB
safety_v2_math_v1.IQ3_XS.gguf	IQ3_XS	3.28GB
safety_v2_math_v1.IQ3_S.gguf	IQ3_S	3.43GB
safety_v2_math_v1.Q3_K_S.gguf	Q3_K_S	3.41GB
safety_v2_math_v1.IQ3_M.gguf	IQ3_M	3.53GB
safety_v2_math_v1.Q3_K.gguf	Q3_K	3.74GB
safety_v2_math_v1.Q3_K_M.gguf	Q3_K_M	3.74GB
safety_v2_math_v1.Q3_K_L.gguf	Q3_K_L	4.03GB
safety_v2_math_v1.IQ4_XS.gguf	IQ4_XS	4.18GB
safety_v2_math_v1.Q4_0.gguf	Q4_0	4.34GB
safety_v2_math_v1.IQ4_NL.gguf	IQ4_NL	4.38GB
safety_v2_math_v1.Q4_K_S.gguf	Q4_K_S	4.37GB
safety_v2_math_v1.Q4_K.gguf	Q4_K	4.58GB
safety_v2_math_v1.Q4_K_M.gguf	Q4_K_M	4.58GB
safety_v2_math_v1.Q4_1.gguf	Q4_1	4.78GB
safety_v2_math_v1.Q5_0.gguf	Q5_0	5.22GB
safety_v2_math_v1.Q5_K_S.gguf	Q5_K_S	5.22GB
safety_v2_math_v1.Q5_K.gguf	Q5_K	5.34GB
safety_v2_math_v1.Q5_K_M.gguf	Q5_K_M	5.34GB
safety_v2_math_v1.Q5_1.gguf	Q5_1	5.65GB
safety_v2_math_v1.Q6_K.gguf	Q6_K	6.14GB
safety_v2_math_v1.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers license: other base_model: meta-llama/Llama-3.1-8B-Instruct tags:

llama-factory
full
generated_from_trainer model-index:
name: safety-v2-math-v1-lr1e-6-ep5 results: []

safety-v2-math-v1-lr1e-6-ep5

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the safety_v2_math_v1 dataset.

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-06
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_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 4.45.2
Pytorch 2.3.0+cu121
Datasets 2.21.0
Tokenizers 0.20.1