MohammadOthman
/

falcon-7b-instruct-truthfulqa

@@ -1,202 +1,73 @@
 ---
-library_name: peft
-base_model: tiiuae/falcon-7b-instruct
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.11.2.dev0

 ---
+language: en
+datasets:
+- truthful_qa
+license: apache-2.0
+tags:
+- qlora
+- falcon
+- fine-tuning
+- nlp
+- causal-lm
+- h100
 ---
+# Falcon-7B QLoRA Fine-Tuned on TruthfulQA
+## Model Description
+This model is a fine-tuned version of the `tiiuae/falcon-7b-instruct` using the QLoRA technique on the [TruthfulQA](https://huggingface.co/datasets/truthful_qa) dataset.
+## Training
+- **Base Model**: `tiiuae/falcon-7b-instruct`
+- **Dataset**: [TruthfulQA](https://huggingface.co/datasets/truthful_qa)
+- **Training Technique**: QLoRA
+- **Hardware**: H100 GPUs
+- **Epochs**: 10
+- **Batch Size**: 16
+- **Learning Rate**: 2e-4
+### How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load the base model
+base_model_name = "tiiuae/falcon-7b-instruct"
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+# Load the adapter and apply it to the base model
+adapter_repo_name = "MohammadOthman/falcon-7b-qlora-truthfulqa"
+model = PeftModel.from_pretrained(base_model, adapter_repo_name)
+# Move model to GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+# Function to generate text
+def generate_text(prompt, max_length=100, num_return_sequences=1):
+    # Tokenize the input prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    # Generate text
+    outputs = model.generate(
+        input_ids=inputs["input_ids"],
+        attention_mask=inputs["attention_mask"],
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        do_sample=True,
+        temperature=0.7
+    )
+    # Decode and print the output
+    for i, output in enumerate(outputs):
+        print(f"Generated Text {i+1}: {tokenizer.decode(output, skip_special_tokens=True)}")
+# Example usage
+prompt = "Once upon a time in a land far, far away"
+generate_text(prompt)
+```