MiniCPM5-1B-Quanto-FP8

MiniCPM5-1B-Quanto-FP8

This repository provides openbmb/MiniCPM5-1B quantized to FP8 (Float8 e4m3fn) using optimum-quanto.

It is optimized for 4th Generation Tensor Cores (NVIDIA Ada Lovelace RTX 40xx series, Hopper H100, Blackwell B200) to deliver ultra-low time-to-first-token (TTFT 76ms), 1.78x higher decode throughput (28.3 tok/s), and a compact 1.4GB VRAM footprint without any loss in reasoning, algebra, or anti-hallucination accuracy compared to native bfloat16.

• Base Model: openbmb/MiniCPM5-1B (1.16B parameters, 128k context, LlamaForCausalLM)
• Quantization Method: optimum-quanto FP8 (weights=qfloat8_e4m3fn, activations=None)
• Model Weight Size: 1.22 GB (model.safetensors)
• Benchmark Evaluation Dataset: aoiandroid/minicpm5-1b-quantization-benchmark

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Performance Benchmarks (NVIDIA RTX 4070 Ti 12GB)

Measured across 8 authentic conversational, logical, mathematical, and structural scenarios:

Why FP8 Outperforms BitsAndBytes 4bit:

1. Hardware Tensor Core Execution: On Ada Lovelace GPUs, FP8 GEMM is executed directly on 4th-gen Tensor Cores.
2. Elimination of Dequantization Overhead: Traditional 4-bit (BitsAndBytes NF4) suffers from dynamic dequantization kernel overhead before every GEMM operation, causing an inverted slowdown on small (1B) models.
3. Zero Accuracy Loss: Float8 e4m3fn preserves dynamic range across attention projections, preventing the logic collapse observed in INT4.

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Quickstart Inference Code

Installation

pip install torch transformers accelerate optimum-quanto

Direct Loading with optimum.quanto

import torch
from optimum.quanto import QuantizedModelForCausalLM
from transformers import AutoTokenizer

model_id = "aoiandroid/MiniCPM5-1B-Quanto-FP8"

# 1. Load model and move to CUDA
model = QuantizedModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
model.to("cuda")

# 2. Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# 3. Format prompt using chat template
prompt = "こんにちは！自己紹介をしてください。"
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("cuda")
inputs.pop("token_type_ids", None)

# 4. Generate with high-speed FP8 Tensor Cores
with torch.no_grad():
    output_tokens = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.7,
        top_p=0.8
    )

response = tokenizer.decode(output_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

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Evaluation Data and Reproducibility

For full raw benchmark output logs (JSON) and evaluation scripts across all 8 test cases, visit the evaluation dataset repository:

• aoiandroid/minicpm5-1b-quantization-benchmark