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Vivekanandao1_finetuned
Henit007/Vivekanandao1_finetuned
Vivekanandao1_finetuned is a machine learning model from Henit007. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This model is a fine-tuned version of the modelname base model using QLoRA (Quantized Low-Rank Adaptation) for efficient and memory-friendly training. Fine-tuning was performed using the Hugging Face trl library’s SFT…
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At a glance
- Model type
- llama
- Access
- Public
- Created
- Aug 8, 2025
- Updated
- Aug 20, 2025
- SHA
- cfe7759b
Model card
From the Hugging Face model README
🧠 Fine-tuned LLaMA Model using QLoRA & LoRA (Supervised Fine-Tuning)
This model is a fine-tuned version of the model_name base model using QLoRA (Quantized Low-Rank Adaptation) for efficient and memory-friendly training. Fine-tuning was performed using the Hugging Face trl library’s SFTTrainer and peft (LoRA).
📌 Model Overview
- Base Model:
model_name - Fine-tuning Method: QLoRA + LoRA (PEFT)
- Task: Causal Language Modeling
- Quantization: 4-bit (bitsandbytes)
- Frameworks: Transformers, PEFT, TRL
🧠 Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Henit007/Vivekanandao1_finetuned")
model = AutoModelForCausalLM.from_pretrained("Henit007/Vivekanandao1_finetuned", device_map="auto")
input_text = "Explain climate change in simple terms."
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Type
- llama
- Created
- Aug 8, 2025
- Updated
- Aug 20, 2025