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Model: g023/Qwen3-1.77B-g023-NF4
Source: Original Platform

model.py

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+# Tweakable parameters
+# MODEL_PATH = "./Qwen3-NF4" # local version
+MODEL_PATH = "g023/Qwen3-1.77B-g023-NF4"
+MAX_NEW_TOKENS = 2000
+TEMPERATURE = 0.7
+DO_SAMPLE = True
+TOP_P = 0.9
+TOP_K = 50
+REPETITION_PENALTY = 1.1
+STREAMING = True  # Set to True for streaming inference
+INPUT_MESSAGE = "You are completing the next step in a task to create an arcade game in javascript. Your available tools are rationalize, red_green_tdd, and create_plan. Synthesize their output when reasoning. "
+
+import sys
+import os
+sys.path.append(os.path.dirname(__file__))
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+def load_model():
+    print("Loading model...")
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_PATH,
+        device_map="auto",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+    print("Model loaded.")
+    return model, tokenizer
+
+def inference_non_streaming(model, tokenizer, messages):
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=MAX_NEW_TOKENS,
+        temperature=TEMPERATURE,
+        do_sample=DO_SAMPLE,
+        top_p=TOP_P,
+        top_k=TOP_K,
+        repetition_penalty=REPETITION_PENALTY,
+    )
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    print("Response:", response)
+
+def inference_streaming(model, tokenizer, messages):
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=MAX_NEW_TOKENS,
+        temperature=TEMPERATURE,
+        do_sample=DO_SAMPLE,
+        top_p=TOP_P,
+        top_k=TOP_K,
+        repetition_penalty=REPETITION_PENALTY,
+        streamer=streamer,
+    )
+
+if __name__ == "__main__":
+    model, tokenizer = load_model()
+    messages = [{"role": "user", "content": INPUT_MESSAGE}]
+    if STREAMING:
+        inference_streaming(model, tokenizer, messages)
+    else:
+        inference_non_streaming(model, tokenizer, messages)