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llama400m-climblab-function_calling-5k-filtered-dora-adapter
leonzc/llama400m-climblab-function_calling-5k-filtered-dora-adapter
llama400m-climblab-function_calling-5k-filtered-dora-adapter is a machine learning model from leonzc. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
Adapter only for DoRA fine-tuned LLaMA 400M model on filtered 5k data from functioncallingeval dataset using LMFlow This is the DoRA adapter for leonzc/llama400m-climblab-functioncalling-5k-filtered-dora-merged.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Aug 27, 2025
- Updated
- Aug 27, 2025
- SHA
- 185f3103
Base models
Model card
From the Hugging Face model README
llama400m-climblab-function_calling-5k-filtered-dora-adapter
Adapter only for DoRA fine-tuned LLaMA 400M model on filtered 5k data from functioncalling_eval dataset using LMFlow
Adapter Details
This is the DoRA adapter for leonzc/llama400m-climblab-function_calling-5k-filtered-dora-merged.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load the base model first
base_model = AutoModelForCausalLM.from_pretrained("data4elm/Llama-400M-12L")
# Load the DoRA adapter
model = PeftModel.from_pretrained(base_model, "leonzc/llama400m-climblab-function_calling-5k-filtered-dora-adapter")
# Load the tokenizer from the base model
tokenizer = AutoTokenizer.from_pretrained("data4elm/Llama-400M-12L")
# Example usage
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Library
- peft
- License
- apache-2.0
- Languages
- en
- Created
- Aug 27, 2025
- Updated
- Aug 27, 2025