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DeeWoo_-_Llama-2-7b-chat_FFT_CodeAlpaca-20k-gguf – AI Model by RichardErkhov | AlphaNeural AI
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DeeWoo_-_Llama-2-7b-chat_FFT_CodeAlpaca-20k-gguf
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Quantization made by Richard Erkhov.
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Llama-2-7b-chat_FFT_CodeAlpaca-20k - GGUF
Model creator:
https://huggingface.co/DeeWoo/
Original model:
https://huggingface.co/DeeWoo/Llama-2-7b-chat_FFT_CodeAlpaca-20k/
Name
Quant method
Size
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q2_K.gguf
Q2_K
2.36GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.IQ3_XS.gguf
IQ3_XS
2.6GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.IQ3_S.gguf
IQ3_S
2.75GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q3_K_S.gguf
Q3_K_S
2.75GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.IQ3_M.gguf
IQ3_M
2.9GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q3_K.gguf
Q3_K
3.07GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q3_K_M.gguf
Q3_K_M
3.07GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q3_K_L.gguf
Q3_K_L
3.35GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.IQ4_XS.gguf
IQ4_XS
3.4GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q4_0.gguf
Q4_0
3.56GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.IQ4_NL.gguf
IQ4_NL
3.58GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q4_K_S.gguf
Q4_K_S
3.59GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q4_K.gguf
Q4_K
3.8GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q4_K_M.gguf
Q4_K_M
3.8GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q4_1.gguf
Q4_1
3.95GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q5_0.gguf
Q5_0
4.33GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q5_K_S.gguf
Q5_K_S
4.33GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q5_K.gguf
Q5_K
4.45GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q5_K_M.gguf
Q5_K_M
4.45GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q5_1.gguf
Q5_1
4.72GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q6_K.gguf
Q6_K
5.15GB
Llama-2-7b-chat_FFT_CodeAlpaca-20k.Q8_0.gguf
Q8_0
6.67GB
Original model description:
library_name: transformers license: other base_model: meta-llama/Llama-2-7b-chat-hf tags:
llama-factory
full
generated_from_trainer model-index:
name: llama2_fft_CodeAlpaca-20k_v5_task results: [] datasets:
sahil2801/CodeAlpaca-20k
llama2_fft_CodeAlpaca-20k_v5_task
This model is a fine-tuned version of
meta-llama/Llama-2-7b-chat-hf
on the CodeAlpaca-20k 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-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
num_epochs: 3.0
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1
