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sedrickkeh_-_mistral_alpaca_sft_sample-gguf – AI Model by RichardErkhov | AlphaNeural AI
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sedrickkeh_-_mistral_alpaca_sft_sample-gguf
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Quantization made by Richard Erkhov.
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mistral_alpaca_sft_sample - GGUF
Model creator:
https://huggingface.co/sedrickkeh/
Original model:
https://huggingface.co/sedrickkeh/mistral_alpaca_sft_sample/
Name
Quant method
Size
mistral_alpaca_sft_sample.Q2_K.gguf
Q2_K
2.53GB
mistral_alpaca_sft_sample.IQ3_XS.gguf
IQ3_XS
2.81GB
mistral_alpaca_sft_sample.IQ3_S.gguf
IQ3_S
2.96GB
mistral_alpaca_sft_sample.Q3_K_S.gguf
Q3_K_S
2.95GB
mistral_alpaca_sft_sample.IQ3_M.gguf
IQ3_M
3.06GB
mistral_alpaca_sft_sample.Q3_K.gguf
Q3_K
3.28GB
mistral_alpaca_sft_sample.Q3_K_M.gguf
Q3_K_M
3.28GB
mistral_alpaca_sft_sample.Q3_K_L.gguf
Q3_K_L
3.56GB
mistral_alpaca_sft_sample.IQ4_XS.gguf
IQ4_XS
3.67GB
mistral_alpaca_sft_sample.Q4_0.gguf
Q4_0
3.83GB
mistral_alpaca_sft_sample.IQ4_NL.gguf
IQ4_NL
3.87GB
mistral_alpaca_sft_sample.Q4_K_S.gguf
Q4_K_S
3.86GB
mistral_alpaca_sft_sample.Q4_K.gguf
Q4_K
4.07GB
mistral_alpaca_sft_sample.Q4_K_M.gguf
Q4_K_M
4.07GB
mistral_alpaca_sft_sample.Q4_1.gguf
Q4_1
4.24GB
mistral_alpaca_sft_sample.Q5_0.gguf
Q5_0
4.65GB
mistral_alpaca_sft_sample.Q5_K_S.gguf
Q5_K_S
4.65GB
mistral_alpaca_sft_sample.Q5_K.gguf
Q5_K
4.78GB
mistral_alpaca_sft_sample.Q5_K_M.gguf
Q5_K_M
4.78GB
mistral_alpaca_sft_sample.Q5_1.gguf
Q5_1
5.07GB
mistral_alpaca_sft_sample.Q6_K.gguf
Q6_K
5.53GB
mistral_alpaca_sft_sample.Q8_0.gguf
Q8_0
7.17GB
Original model description:
library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags:
llama-factory
generated_from_trainer model-index:
name: mistral_alpaca_sft_sample results: []
mistral_alpaca_sft_sample
This model is a fine-tuned version of
mistralai/Mistral-7B-v0.1
on an unknown 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: 8
gradient_accumulation_steps: 4
total_train_batch_size: 512
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
training_steps: 3
Training results
Training Loss
Epoch
Step
Validation Loss
No log
0.1304
3
1.6965
Framework versions
Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 2.21.0
Tokenizers 0.20.1
