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Ellight_-_code-oute-1-65m-text-to-sql-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Ellight_-_code-oute-1-65m-text-to-sql-gguf
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Quantization made by Richard Erkhov.
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code-oute-1-65m-text-to-sql - GGUF
Model creator:
https://huggingface.co/Ellight/
Original model:
https://huggingface.co/Ellight/code-oute-1-65m-text-to-sql/
Name
Quant method
Size
code-oute-1-65m-text-to-sql.Q2_K.gguf
Q2_K
0.03GB
code-oute-1-65m-text-to-sql.IQ3_XS.gguf
IQ3_XS
0.03GB
code-oute-1-65m-text-to-sql.IQ3_S.gguf
IQ3_S
0.03GB
code-oute-1-65m-text-to-sql.Q3_K_S.gguf
Q3_K_S
0.03GB
code-oute-1-65m-text-to-sql.IQ3_M.gguf
IQ3_M
0.03GB
code-oute-1-65m-text-to-sql.Q3_K.gguf
Q3_K
0.03GB
code-oute-1-65m-text-to-sql.Q3_K_M.gguf
Q3_K_M
0.03GB
code-oute-1-65m-text-to-sql.Q3_K_L.gguf
Q3_K_L
0.04GB
code-oute-1-65m-text-to-sql.IQ4_XS.gguf
IQ4_XS
0.04GB
code-oute-1-65m-text-to-sql.Q4_0.gguf
Q4_0
0.04GB
code-oute-1-65m-text-to-sql.IQ4_NL.gguf
IQ4_NL
0.04GB
code-oute-1-65m-text-to-sql.Q4_K_S.gguf
Q4_K_S
0.04GB
code-oute-1-65m-text-to-sql.Q4_K.gguf
Q4_K
0.04GB
code-oute-1-65m-text-to-sql.Q4_K_M.gguf
Q4_K_M
0.04GB
code-oute-1-65m-text-to-sql.Q4_1.gguf
Q4_1
0.04GB
code-oute-1-65m-text-to-sql.Q5_0.gguf
Q5_0
0.04GB
code-oute-1-65m-text-to-sql.Q5_K_S.gguf
Q5_K_S
0.04GB
code-oute-1-65m-text-to-sql.Q5_K.gguf
Q5_K
0.04GB
code-oute-1-65m-text-to-sql.Q5_K_M.gguf
Q5_K_M
0.04GB
code-oute-1-65m-text-to-sql.Q5_1.gguf
Q5_1
0.05GB
code-oute-1-65m-text-to-sql.Q6_K.gguf
Q6_K
0.05GB
code-oute-1-65m-text-to-sql.Q8_0.gguf
Q8_0
0.06GB
Original model description:
library_name: transformers license: apache-2.0 base_model: OuteAI/Lite-Oute-1-65M tags:
trl
sft
generated_from_trainer datasets:
generator model-index:
name: code-oute-1-65m-text-to-sql results: []
code-oute-1-65m-text-to-sql
This model is a fine-tuned version of
OuteAI/Lite-Oute-1-65M
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: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 3
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1
