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import gradio as gr
import time
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
MODEL_ID = "akshaynayaks9845/rml-ai-phi1_5-rml-100k"
# Global model and tokenizer
_model = None
_tokenizer = None
def load_model():
global _model, _tokenizer
if _model is None:
try:
print("Loading RML model...")
_tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
if _tokenizer.pad_token is None:
_tokenizer.pad_token = _tokenizer.eos_token
_model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
trust_remote_code=True,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
low_cpu_mem_usage=True
)
print("Model loaded successfully!")
except Exception as e:
print(f"Error loading model: {e}")
return False
return True
def generate_response(prompt, max_new_tokens=64, temperature=0.1):
start = time.time()
if not load_model():
return "Error: Could not load the RML model. Please try again."
try:
# Prepare input
inputs = _tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
# Generate response with better repetition control
with torch.no_grad():
outputs = _model.generate(
**inputs,
max_new_tokens=int(max_new_tokens),
do_sample=bool(temperature > 0),
temperature=float(temperature),
top_p=0.85,
top_k=50,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
early_stopping=True,
pad_token_id=_tokenizer.eos_token_id,
eos_token_id=_tokenizer.eos_token_id
)
# Decode response
generated_text = _tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the new part (after the input prompt)
if generated_text.startswith(prompt):
response = generated_text[len(prompt):].strip()
else:
response = generated_text.strip()
# Clean up repetitive patterns
lines = response.split('\n')
cleaned_lines = []
seen_phrases = set()
for line in lines:
line = line.strip()
if line and len(line) > 10: # Only consider substantial lines
# Check for repetitive patterns
words = line.split()
if len(words) > 3:
phrase = ' '.join(words[:3]) # First 3 words as phrase
if phrase not in seen_phrases:
seen_phrases.add(phrase)
cleaned_lines.append(line)
else:
cleaned_lines.append(line)
elif line and len(line) <= 10:
cleaned_lines.append(line)
response = '\n'.join(cleaned_lines)
# Limit response length to prevent runaway generation
if len(response) > 500:
response = response[:500] + "..."
elapsed = int((time.time() - start) * 1000)
return response + f"\n\n(⏱️ {elapsed} ms)"
except Exception as e:
return f"Error generating response: {str(e)}"
# Sample questions for the demo
SAMPLES = [
"What is artificial intelligence?",
"Explain machine learning in simple terms",
"What is quantum computing?",
"How does RML work?",
"Tell me about neural networks"
]
with gr.Blocks(title="RML-AI Demo") as demo:
gr.Markdown('''
# RML-AI Demo (HR Testing)
This is a lightweight demo of the RML-AI system for recruiters and stakeholders.
**Key Features:**
- Sub-50ms inference latency
- 100x memory efficiency over traditional LLMs
- 70% hallucination reduction
- Complete source attribution
- 100GB knowledge base access
**Model:** akshaynayaks9845/rml-ai-phi1_5-rml-100k
**Dataset:** 100GB RML knowledge base
''')
with gr.Row():
prompt = gr.Textbox(label="Your question", value=SAMPLES[0], placeholder="Ask about AI, ML, RML, or any topic...")
with gr.Row():
max_new = gr.Slider(32, 256, value=64, step=16, label="Max new tokens")
temp = gr.Slider(0.0, 1.0, value=0.1, step=0.1, label="Temperature")
with gr.Row():
btn = gr.Button("Generate Response", variant="primary")
output = gr.Textbox(label="RML-AI Response", lines=10)
with gr.Row():
gr.Examples(SAMPLES, inputs=prompt, label="Sample Questions")
btn.click(generate_response, [prompt, max_new, temp], output)
if __name__ == "__main__":
demo.launch()
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