DeepSeek Lite Chat Inference Script | Generated by AI

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#!/usr/bin/env python3 “”” DeepSeek-V2-Lite-Chat inference with 4-bit quantization. Fits in 12GB VRAM (RTX 4070).

Usage: python run_lite.py # interactive chat python run_lite.py -p “Your prompt here” # single prompt “””

import argparse import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

MODEL_PATH = “/mnt/data/models/DeepSeek-V2-Lite-Chat”

def load_model(): “"”Load model with 4-bit quantization.””” print(“Loading tokenizer…”) tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)

print("Loading model in 4-bit...")
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)

mem_gb = torch.cuda.memory_allocated() / 1024**3
print(f"Model loaded. GPU memory used: {mem_gb:.1f} GB")
return model, tokenizer

def generate(model, tokenizer, prompt, max_new_tokens=512): “"”Generate a response.””” messages = [{“role”: “user”, “content”: prompt}] input_text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(input_text, return_tensors=”pt”).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
    )

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
return response

def interactive(model, tokenizer): “"”Interactive chat loop.””” print(“\nDeepSeek-V2-Lite-Chat (4-bit) — Interactive Mode”) print(“Type ‘quit’ to exit, ‘clear’ to reset history.\n”)

while True:
    try:
        user_input = input("You: ").strip()
    except (EOFError, KeyboardInterrupt):
        print("\nBye!")
        break

    if not user_input:
        continue
    if user_input.lower() == "quit":
        break
    if user_input.lower() == "clear":
        print("Context cleared.\n")
        continue

    response = generate(model, tokenizer, user_input)
    print(f"\nDeepSeek: {response}\n")

def main(): parser = argparse.ArgumentParser(description=”DeepSeek-V2-Lite inference”) parser.add_argument(“-p”, “–prompt”, help=”Single prompt mode”) parser.add_argument( “-n”, “–max-tokens”, type=int, default=512, help=”Max new tokens” ) args = parser.parse_args()

model, tokenizer = load_model()

if args.prompt:
    response = generate(model, tokenizer, args.prompt, args.max_tokens)
    print(response)
else:
    interactive(model, tokenizer)

if name == “main”: main()

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