rfvasile/linalgzero-sft
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How to use rfvasile/LinalgZero-SFT-LoRA with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rfvasile/LinalgZero-SFT-LoRA", dtype="auto")How to use rfvasile/LinalgZero-SFT-LoRA 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 rfvasile/LinalgZero-SFT-LoRA 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 rfvasile/LinalgZero-SFT-LoRA to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rfvasile/LinalgZero-SFT-LoRA to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="rfvasile/LinalgZero-SFT-LoRA",
max_seq_length=2048,
)The training code is available on Github.
This model is a fine-tuned version of Qwen/Qwen2.5-3B on the atomwalk12/linalgzero-sft dataset. It has been trained using TRL.
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="atomwalk12/LinalgZero-SFT-LoRA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
This model was trained with SFT.