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README.md

@@ -1,12 +1,15 @@
 ---
-title: Test Chatbot 2
-emoji: 😻
-colorFrom: gray
-colorTo: blue
+title: Test Chatbot 1
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
 sdk: gradio
-sdk_version: 5.43.1
+sdk_version: 5.42.0
 app_file: app.py
 pinned: false
+hf_oauth: true
+hf_oauth_scopes:
+- inference-api
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -0,0 +1,93 @@
+import spaces
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, TorchAoConfig
+from threading import Thread
+import torch
+from torchao.quantization import Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig
+
+quant_config = Int8DynamicActivationInt8WeightConfig()
+#quant_config = Int8WeightOnlyConfig()
+quantization_config = TorchAoConfig(quant_type=quant_config)
+#checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
+checkpoint = "unsloth/gemma-3-4b-it"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+#model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32).to(device)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+                                             device_map=device, quantization_config=quantization_config)
+
+@spaces.GPU(duration=30)
+def respond_stream(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}] + history + [{"role": "user", "content": message}]
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    streamer = TextIteratorStreamer(
+        tokenizer, skip_prompt=True, skip_special_tokens=True
+    )
+    gen_kwargs = dict(
+        inputs=input_ids,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        eos_token_id=tokenizer.eos_token_id,
+        cache_implementation="static",
+    )
+    thread = Thread(target=model.generate, kwargs=gen_kwargs)
+    thread.start()
+
+    partial = ""
+    for piece in streamer:
+        partial += piece
+        yield partial
+
+@spaces.GPU(duration=30)
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}] + history + [{"role": "user", "content": message}]
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    outputs = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=max_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        eos_token_id=tokenizer.eos_token_id,
+        cache_implementation="static",
+    )
+
+    gen_ids = outputs[0][input_ids.shape[-1]:]
+    return tokenizer.decode(gen_ids, skip_special_tokens=True)
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
+)
+
+with gr.Blocks() as demo:
+    chatbot.render()
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -0,0 +1,8 @@
+huggingface_hub[hf_xet]
+torch
+torchao
+transformers
+accelerate
+peft
+sentencepiece
+pydantic==2.10.6