Reinforcement Learning
PEFT
Safetensors
qwen2
openenv
logistics
grpo
unsloth
trl
4-bit precision
bitsandbytes
Instructions to use Leavin1611/logistics-hackathon-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Leavin1611/logistics-hackathon-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Leavin1611/logistics-hackathon-model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use Leavin1611/logistics-hackathon-model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Leavin1611/logistics-hackathon-model to start chatting
Install 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 Leavin1611/logistics-hackathon-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Leavin1611/logistics-hackathon-model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Leavin1611/logistics-hackathon-model", max_seq_length=2048, )
| { | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "max_length": 32768, | |
| "pad_token_id": 151665, | |
| "repetition_penalty": 1.1, | |
| "temperature": 0.7, | |
| "top_k": 20, | |
| "top_p": 0.8, | |
| "transformers_version": "5.5.0" | |
| } | |