Beta
Explore
Marketplace
Neural Labs
Chat
Wallet
Docs
LLaVA-Mistral-finetuned – AI Model by vvduc03 | AlphaNeural AI
You can deploy this model and start earning money today!
vvduc03
/
LLaVA-Mistral-finetuned
like
0
peft
safetensors
generated_from_trainer
liuhaotian/llava-v1.6-mistral-7b
adapter
apache-2.0
us
Views
No views yet
Model card
Files and Versions
Community
API
Deploy
LLaVA-Mistral-finetuned
This model is a fine-tuned version of
liuhaotian/llava-v1.6-mistral-7b
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.0000
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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 200
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
0.0
200.0
200
0.0000
Framework versions
Transformers 4.42.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.14.5
Tokenizers 0.19.1
Training procedure
The following
bitsandbytes
quantization config was used during training:
quant_method: QuantizationMethod.BITS_AND_BYTES
_load_in_8bit: False
_load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: float16
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False
Framework versions
PEFT 0.6.2
