adds harmony in template format

app.py

@@ -12,7 +12,7 @@ try:
         "openai/gpt-oss-20b",
         torch_dtype="auto",
         device_map="auto",
-        attn_implementation="kernel-community/vllm-flash-attention3"
+        attn_implementation="kernels-community/vllm-flash-attention3"
     )
     tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
     
@@ -29,21 +29,6 @@ except Exception as e:
     print(f"❌ Error loading model: {e}")
     raise e
 
-def format_messages(messages):
-    """Format messages into a prompt string"""
-    formatted = ""
-    for message in messages:
-        role = message["role"]
-        content = message["content"]
-        if role == "system":
-            formatted += f"System: {content}\n"
-        elif role == "user":
-            formatted += f"User: {content}\n"
-        elif role == "assistant":
-            formatted += f"Assistant: {content}\n"
-    formatted += "Assistant: "
-    return formatted
-
 def format_conversation_history(chat_history):
     messages = []
     for item in chat_history:
@@ -60,9 +45,11 @@ def generate_response(input_data, chat_history, max_new_tokens, system_prompt, t
     system_message = [{"role": "system", "content": system_prompt}] if system_prompt else []
     processed_history = format_conversation_history(chat_history)
     messages = system_message + processed_history + [new_message]
-    
-    # Format the prompt
-    prompt = format_messages(messages)
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
     
     # Create streamer for proper streaming
     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
@@ -80,7 +67,7 @@ def generate_response(input_data, chat_history, max_new_tokens, system_prompt, t
         "use_cache": True
     }
     
-    # Tokenize input
+    # Tokenize input using the chat template
     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
     
     # Start generation in a separate thread

app_alternative.py

@@ -29,21 +29,6 @@ except Exception as e:
     print(f"❌ Error loading model: {e}")
     raise e
 
-def format_messages(messages):
-    """Format messages into a prompt string"""
-    formatted = ""
-    for message in messages:
-        role = message["role"]
-        content = message["content"]
-        if role == "system":
-            formatted += f"System: {content}\n"
-        elif role == "user":
-            formatted += f"User: {content}\n"
-        elif role == "assistant":
-            formatted += f"Assistant: {content}\n"
-    formatted += "Assistant: "
-    return formatted
-
 def format_conversation_history(chat_history):
     messages = []
     for item in chat_history:
@@ -61,8 +46,13 @@ def generate_response(input_data, chat_history, max_new_tokens, system_prompt, t
     processed_history = format_conversation_history(chat_history)
     messages = system_message + processed_history + [new_message]
     
-    # Format the prompt
-    prompt = format_messages(messages)
+    # Use the model's chat template to format the conversation properly
+    # This is crucial for GPT-OSS-20B which expects the Harmony format
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
     
     # Alternative streaming approach with manual chunking
     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

app_harmony.py

@@ -0,0 +1,203 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import torch
+from threading import Thread
+import gradio as gr
+import spaces
+import re
+from peft import PeftModel
+
+# Load the base model
+try:
+    base_model = AutoModelForCausalLM.from_pretrained(
+        "openai/gpt-oss-20b",
+        torch_dtype="auto",
+        device_map="auto",
+        attn_implementation="kernels-community/vllm-flash-attention3"
+    )
+    tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
+    
+    # Load the LoRA adapter
+    try:
+        model = PeftModel.from_pretrained(base_model, "Tonic/gpt-oss-20b-multilingual-reasoner")
+        print("✅ LoRA model loaded successfully!")
+    except Exception as lora_error:
+        print(f"⚠️ LoRA adapter failed to load: {lora_error}")
+        print("🔄 Falling back to base model...")
+        model = base_model
+        
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    raise e
+
+def format_conversation_history(chat_history):
+    messages = []
+    for item in chat_history:
+        role = item["role"]
+        content = item["content"]
+        if isinstance(content, list):
+            content = content[0]["text"] if content and "text" in content[0] else str(content)
+        messages.append({"role": role, "content": content})
+    return messages
+
+def create_harmony_prompt(messages, reasoning_level="medium"):
+    """
+    Create a proper Harmony format prompt for GPT-OSS-20B
+    Based on the Harmony format from https://github.com/openai/harmony
+    """
+    # Start with system message in Harmony format
+    system_content = f"""You are ChatGPT, a large language model trained by OpenAI.
+Knowledge cutoff: 2024-06
+Current date: 2025-01-28
+
+Reasoning: {reasoning_level}
+
+# Valid channels: analysis, commentary, final. Channel must be included for every message."""
+    
+    # Build the prompt in Harmony format
+    prompt_parts = []
+    
+    # Add system message
+    prompt_parts.append(f"<|start|>system<|message|>{system_content}<|end|>")
+    
+    # Add conversation messages
+    for message in messages:
+        role = message["role"]
+        content = message["content"]
+        
+        if role == "system":
+            # Skip system messages as we already added the main one
+            continue
+        elif role == "user":
+            prompt_parts.append(f"<|start|>user<|message|>{content}<|end|>")
+        elif role == "assistant":
+            prompt_parts.append(f"<|start|>assistant<|message|>{content}<|end|>")
+    
+    # Add the generation prompt
+    prompt_parts.append("<|start|>assistant")
+    
+    return "\n".join(prompt_parts)
+
+@spaces.GPU(duration=60)
+def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
+    new_message = {"role": "user", "content": input_data}
+    system_message = [{"role": "system", "content": system_prompt}] if system_prompt else []
+    processed_history = format_conversation_history(chat_history)
+    messages = system_message + processed_history + [new_message]
+    
+    # Extract reasoning level from system prompt
+    reasoning_level = "medium"
+    if "reasoning:" in system_prompt.lower():
+        if "high" in system_prompt.lower():
+            reasoning_level = "high"
+        elif "low" in system_prompt.lower():
+            reasoning_level = "low"
+    
+    # Create Harmony format prompt
+    prompt = create_harmony_prompt(messages, reasoning_level)
+    
+    # Create streamer for proper streaming
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    
+    # Prepare generation kwargs
+    generation_kwargs = {
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+        "pad_token_id": tokenizer.eos_token_id,
+        "streamer": streamer,
+        "use_cache": True
+    }
+    
+    # Tokenize input using the Harmony format
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Start generation in a separate thread
+    thread = Thread(target=model.generate, kwargs={**inputs, **generation_kwargs})
+    thread.start()
+    
+    # Stream the response and parse Harmony format
+    current_channel = None
+    current_content = ""
+    thinking = ""
+    final = ""
+    
+    for chunk in streamer:
+        current_content += chunk
+        
+        # Parse Harmony format channels
+        # Look for channel markers like <|channel|>analysis, <|channel|>commentary, <|channel|>final
+        if "<|channel|>" in current_content:
+            # Extract channel and content
+            parts = current_content.split("<|channel|>")
+            if len(parts) >= 2:
+                channel_part = parts[1]
+                if channel_part.startswith("analysis"):
+                    current_channel = "analysis"
+                    content_start = channel_part.find("<|message|>")
+                    if content_start != -1:
+                        content = channel_part[content_start + 10:]  # length of "<|message|>"
+                        thinking += content
+                elif channel_part.startswith("commentary"):
+                    current_channel = "commentary"
+                    content_start = channel_part.find("<|message|>")
+                    if content_start != -1:
+                        content = channel_part[content_start + 10:]
+                        thinking += content
+                elif channel_part.startswith("final"):
+                    current_channel = "final"
+                    content_start = channel_part.find("<|message|>")
+                    if content_start != -1:
+                        content = channel_part[content_start + 10:]
+                        final += content
+        
+        # Clean up the content for display
+        clean_thinking = re.sub(r'^analysis\s*', '', thinking).strip()
+        clean_final = final.strip()
+        
+        # Format for display
+        if clean_thinking or clean_final:
+            formatted = f"<details open><summary>Click to view Thinking Process</summary>\n\n{clean_thinking}\n\n</details>\n\n{clean_final}"
+            yield formatted
+
+demo = gr.ChatInterface(
+    fn=generate_response,
+    additional_inputs=[
+        gr.Slider(label="Max new tokens", minimum=64, maximum=4096, step=1, value=2048),
+        gr.Textbox(
+            label="System Prompt",
+            value="You are a helpful assistant. Reasoning: medium",
+            lines=4,
+            placeholder="Change system prompt"
+        ),
+        gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.7),
+        gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
+        gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=50),
+        gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.0)
+    ],
+    examples=[
+        [{"text": "Explain Newton laws clearly and concisely"}],
+        [{"text": "Write a Python function to calculate the Fibonacci sequence"}],
+        [{"text": "What are the benefits of open weight AI models"}],
+    ],
+    cache_examples=False,
+    type="messages",
+    description="""
+# 🙋🏻‍♂️Welcome to 🌟Tonic's gpt-oss-20b Multilingual Reasoner Demo !
+Wait couple of seconds initially. You can adjust reasoning level in the system prompt like "Reasoning: high.
+This version uses the proper Harmony format for better generation quality.
+    """,
+    fill_height=True,
+    textbox=gr.Textbox(
+        label="Query Input",
+        placeholder="Type your prompt"
+    ),
+    stop_btn="Stop Generation",
+    multimodal=False,
+    theme=gr.themes.Soft()
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True) 

requirements.txt

@@ -5,4 +5,5 @@ trl
 bitsandbytes
 triton 
 accelerate
-kernels
+kernels
+openai-harmony