Qwen GRPO Fine Tuning
Collection
3 items • Updated • 1
How to use vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit") # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit")
model = AutoModelForMultimodalLM.from_pretrained("vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit")How to use vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit with Unsloth Studio:
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 vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit to start chatting
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 vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="vinhnx90/vt-qwen-3b-GRPO-merged-16bit-bnb-4bit",
max_seq_length=2048,
)This model is a quantized version of the original model vinhnx90/vt-qwen-3b-GRPO-merged-16bit.
It's quantized using the BitsAndBytes library to 4-bit using the bnb-my-repo space.
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
Base model
vinhnx90/vt-qwen-3b-GRPO-merged-16bit