Beta
Explore
Marketplace
Neural Labs
Chat
Wallet
Docs
mistral-journal-finetune – AI Model by AkashSingh81085 | AlphaNeural AI
You can deploy this model and start earning money today!
AkashSingh81085
/
mistral-journal-finetune
like
0
peft
tensorboard
safetensors
generated_from_trainer
mistralai/Mistral-7B-v0.1
adapter
us
Views
No views yet
Model card
Files and Versions
Community
API
Deploy
mistral-journal-finetune
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
The following
bitsandbytes
quantization config was used during training:
quant_method: bitsandbytes
_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: bfloat16
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False
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: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
training_steps: 10
Training results
Framework versions
PEFT 0.4.0
Transformers 4.42.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1
