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CharlesLi_-_OpenELM-1_1B-SFT-max-reward-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Quantization made by Richard Erkhov.
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OpenELM-1_1B-SFT-max-reward - GGUF
Model creator:
https://huggingface.co/CharlesLi/
Original model:
https://huggingface.co/CharlesLi/OpenELM-1_1B-SFT-max-reward/
Name
Quant method
Size
OpenELM-1_1B-SFT-max-reward.Q2_K.gguf
Q2_K
0.39GB
OpenELM-1_1B-SFT-max-reward.IQ3_XS.gguf
IQ3_XS
0.44GB
OpenELM-1_1B-SFT-max-reward.IQ3_S.gguf
IQ3_S
0.46GB
OpenELM-1_1B-SFT-max-reward.Q3_K_S.gguf
Q3_K_S
0.46GB
OpenELM-1_1B-SFT-max-reward.IQ3_M.gguf
IQ3_M
0.49GB
OpenELM-1_1B-SFT-max-reward.Q3_K.gguf
Q3_K
0.52GB
OpenELM-1_1B-SFT-max-reward.Q3_K_M.gguf
Q3_K_M
0.52GB
OpenELM-1_1B-SFT-max-reward.Q3_K_L.gguf
Q3_K_L
0.56GB
OpenELM-1_1B-SFT-max-reward.IQ4_XS.gguf
IQ4_XS
0.55GB
OpenELM-1_1B-SFT-max-reward.Q4_0.gguf
Q4_0
0.58GB
OpenELM-1_1B-SFT-max-reward.IQ4_NL.gguf
IQ4_NL
0.58GB
OpenELM-1_1B-SFT-max-reward.Q4_K_S.gguf
Q4_K_S
0.58GB
OpenELM-1_1B-SFT-max-reward.Q4_K.gguf
Q4_K
0.63GB
OpenELM-1_1B-SFT-max-reward.Q4_K_M.gguf
Q4_K_M
0.63GB
OpenELM-1_1B-SFT-max-reward.Q4_1.gguf
Q4_1
0.64GB
OpenELM-1_1B-SFT-max-reward.Q5_0.gguf
Q5_0
0.7GB
OpenELM-1_1B-SFT-max-reward.Q5_K_S.gguf
Q5_K_S
0.7GB
OpenELM-1_1B-SFT-max-reward.Q5_K.gguf
Q5_K
0.73GB
OpenELM-1_1B-SFT-max-reward.Q5_K_M.gguf
Q5_K_M
0.73GB
OpenELM-1_1B-SFT-max-reward.Q5_1.gguf
Q5_1
0.76GB
OpenELM-1_1B-SFT-max-reward.Q6_K.gguf
Q6_K
0.83GB
OpenELM-1_1B-SFT-max-reward.Q8_0.gguf
Q8_0
1.07GB
Original model description:
library_name: transformers license: other base_model: apple/OpenELM-1_1B tags:
trl
sft
generated_from_trainer datasets:
generator model-index:
name: OpenELM-1_1B-SFT-max-reward results: []
OpenELM-1_1B-SFT-max-reward
This model is a fine-tuned version of
apple/OpenELM-1_1B
on the generator 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: 2e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 64
total_eval_batch_size: 32
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
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1
