meta-math/MetaMathQA
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How to use witcheer/llama-3.2-1b-gsm8k-lora with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "witcheer/llama-3.2-1b-gsm8k-lora")How to use witcheer/llama-3.2-1b-gsm8k-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 witcheer/llama-3.2-1b-gsm8k-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 witcheer/llama-3.2-1b-gsm8k-lora to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for witcheer/llama-3.2-1b-gsm8k-lora to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="witcheer/llama-3.2-1b-gsm8k-lora",
max_seq_length=2048,
)A small, demonstrative QLoRA adapter that lifts Llama-3.2-1B-Instruct on GSM8K.
meta-llama/Llama-3.2-1B-Instruct (Unsloth 4-bit)#### <answer>This is an in-process eval (internally consistent base-vs-tuned), not a public leaderboard harness — the absolute % is not directly comparable to other boards. The delta is the claim. A small model + a quick QLoRA buys format-alignment and a few points, not a new tier; the value is the rigor (measure honestly, align train to eval).
By WITCHEER · rig: github.com/notwitcheer/llm-bench-rig
Base model
meta-llama/Llama-3.2-1B-Instruct