MiniCPM4.1-8B-AutoAWQ

Image: image

GitHub Repo | Technical Report | Join Us

👋 Contact us in Discord and WeChat

What's New#

• [2025.09.05] MiniCPM4.1 series are released! This series is a hybrid reasoning model, which can be used in

both deep reasoning mode and non-reasoning mode. 🔥🔥🔥

• [2025.06.06] MiniCPM4 series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report here.🔥🔥🔥

MiniCPM4 and MiniCPM4.1 Series#

MiniCPM4 and MiniCPM4.1 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.

• MiniCPM4.1-8B: The latest version of MiniCPM4, with 8B parameters, support fusion thinking.
• MiniCPM4.1-8B-GPTQ: MiniCPM4.1-8B in GPTQ format.
• MiniCPM4.1-8B-AutoAWQ: MiniCPM4.1-8B in AutoAWQ format. (<-- you are here)
• MiniCPM-4.1-8B-Marlin: MiniCPM4.1-8B in Marlin format.
• MiniCPM4.1-8B-GGUF: MiniCPM4.1-8B in GGUF format.
• MiniCPM4.1-8B-MLX: MiniCPM4.1-8B in MLX format.
• MiniCPM4.1-8B-Eagle3: Eagle3 model for MiniCPM4.1-8B.
• MiniCPM4 Series Click to expand all MiniCPM4 series models

• MiniCPM4-8B: The flagship model with 8B parameters, trained on 8T tokens
• MiniCPM4-0.5B: Lightweight version with 0.5B parameters, trained on 1T tokens
• MiniCPM4-8B-Eagle-FRSpec: Eagle head for FRSpec, accelerating speculative inference
• MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu: Eagle head with QAT for FRSpec, integrating speculation and quantization for ultra acceleration
• MiniCPM4-8B-Eagle-vLLM: Eagle head in vLLM format for speculative inference
• MiniCPM4-8B-marlin-Eagle-vLLM: Quantized Eagle head for vLLM format
• BitCPM4-0.5B: Extreme ternary quantization of MiniCPM4-0.5B, achieving 90% bit width reduction
• BitCPM4-1B: Extreme ternary quantization of MiniCPM3-1B, achieving 90% bit width reduction
• MiniCPM4-Survey: Generates trustworthy, long-form survey papers from user queries
• MiniCPM4-MCP: Integrates MCP tools to autonomously satisfy user requirements

Introduction#

MiniCPM4 and MiniCPM4.1 are extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.

• 🏗️ Efficient Model Architecture:
  • InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
• 🧠 Efficient Learning Algorithms:
  • Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
  • BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
  • Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
• 📚 High-Quality Training Data:
  • UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset UltraFinweb
  • UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
• ⚡ Efficient Inference System:
  • CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
  • ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities

Usage#

Prebuilt AutoAWQ#

pip install autoawq

Inference#

from awq import AutoAWQForCausalLM
import torch
from transformers import AutoTokenizer

prompt = "北京有什么好玩的地方？"
quant_path = "MiniCPM4.1-8B-AutoAWQ"

messages = [{"role": "user", "content": prompt}]

model = AutoAWQForCausalLM.from_quantized(
    quant_path,
    fuse_layers=False, 
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    quant_path,
    trust_remote_code=True
)
device = next(model.model.parameters()).device

# if open think mode, use the following code
formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt = True, enable_thinking = True)
# if close think mode, use the following code
# formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt = True, enable_thinking = False)

input_ids = tokenizer.encode(formatted_prompt, return_tensors='pt').to(device)
outputs = model.generate(
    input_ids,
    max_new_tokens=1000,
    do_sample=True
)
# if open think mode, use the following code
ans = [i.split("<|im_start|> assistant\n", 1)[1].strip() for i in tokenizer.batch_decode(outputs)]
# if close think mode, use the following code
# ans = [i.split("<|im_start|> assistant\n<think>\n\n</think>", 1)[1].strip() for i in tokenizer.batch_decode(outputs)]