Gpt2 Medium Wikitext2 Lora

38
1
1 language
license:mit
by
shiva9876
Language Model
OTHER
New
38 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
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Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Code Examples

Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")
Merging LoRA Weights (Optional)python
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")
tokenizer.save_pretrained("./merged_model")

# Load merged model directly
model = AutoModelForCausalLM.from_pretrained("./merged_model")

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