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 Desktop
Download tokenizer.json from Leavin1611/logistics-hackathon-model: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Leavin1611/logistics-hackathon-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Leavin1611/logistics-hackathon-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Leavin1611/logistics-hackathon-model/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- fa97c8db43bf1c5104fadca317fa93e149bdb82d4df408d168ad848277b0b15d
- Size of remote file:
- 11.4 MB
- SHA256:
- af5891a15588546db1ac7f2baf8fa94835a51a85c032c39793a55bb048b47446
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