PEFT
Safetensors
mistral
alignment-handbook
trl
sft
unsloth
Generated from Trainer
4-bit precision
bitsandbytes
How to use from
Unsloth StudioInstall Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Peter/uploadtestsmallstep2 to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Peter/uploadtestsmallstep2 to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Peter/uploadtestsmallstep2",
max_seq_length=2048,
)Quick Links
uploadtestsmallstep2
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the zeta-labs/mind2web_combined_236_18_01 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3438
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 5
Training results
Framework versions
- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
- 6
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Model tree for Peter/uploadtestsmallstep2
Base model
unsloth/mistral-7b
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Peter/uploadtestsmallstep2 to start chatting