说明文档

示例测试

import onnxruntime as ort
import numpy as np
from transformers import AutoTokenizer

# 加载分词器
tokenizer = AutoTokenizer.from_pretrained("CohenQu/DeepSeek-R1-Distill-Qwen-7B-GRPO")

# 加载 ONNX 模型
onnx_model_path = "model.onnx"  # ONNX 模型路径
session = ort.InferenceSession(onnx_model_path)
question = "如果你有一台时光机，但只能去往过去或未来一次且无法返回，你会选择哪个，为什么？"
inputs = tokenizer(question, return_tensors="np", padding=True)
output = session.run(
    None,  # 输出名称可以为 None 以获取所有输出
    {'input_ids': inputs['input_ids'],'attention_mask': inputs['attention_mask']}
)[0]
generated_text = tokenizer.decode(np.argmax(output, axis=-1)[0], skip_special_tokens=True)
print("生成的文本:", generated_text)

cfu/Qwen-1.5B-onnx

作者 cfu

↓ 1 ♥ 0

创建时间: 2025-03-31 15:32:22+00:00

更新时间: 2025-03-31 17:20:01+00:00

在 Hugging Face 上查看

文件 (290)

.gitattributes

README.md

config.json

model.embed_tokens.weight

model.layers.0.input_layernorm.weight

model.layers.0.post_attention_layernorm.weight

model.layers.0.self_attn.q_proj.bias

model.layers.1.input_layernorm.weight

model.layers.1.post_attention_layernorm.weight

model.layers.1.self_attn.q_proj.bias

model.layers.10.input_layernorm.weight

model.layers.10.post_attention_layernorm.weight

model.layers.10.self_attn.q_proj.bias

model.layers.11.input_layernorm.weight

model.layers.11.post_attention_layernorm.weight

model.layers.11.self_attn.q_proj.bias

model.layers.12.input_layernorm.weight

model.layers.12.post_attention_layernorm.weight

model.layers.12.self_attn.q_proj.bias

model.layers.13.input_layernorm.weight

model.layers.13.post_attention_layernorm.weight

model.layers.13.self_attn.q_proj.bias

model.layers.14.input_layernorm.weight

model.layers.14.post_attention_layernorm.weight

model.layers.14.self_attn.q_proj.bias

model.layers.15.input_layernorm.weight

model.layers.15.post_attention_layernorm.weight

model.layers.15.self_attn.q_proj.bias

model.layers.16.input_layernorm.weight

model.layers.16.post_attention_layernorm.weight

model.layers.16.self_attn.q_proj.bias

model.layers.17.input_layernorm.weight

model.layers.17.post_attention_layernorm.weight

model.layers.17.self_attn.q_proj.bias

model.layers.18.input_layernorm.weight

model.layers.18.post_attention_layernorm.weight

model.layers.18.self_attn.q_proj.bias

model.layers.19.input_layernorm.weight

model.layers.19.post_attention_layernorm.weight

model.layers.19.self_attn.q_proj.bias

model.layers.2.input_layernorm.weight

model.layers.2.post_attention_layernorm.weight

model.layers.2.self_attn.q_proj.bias

model.layers.20.input_layernorm.weight

model.layers.20.post_attention_layernorm.weight

model.layers.20.self_attn.q_proj.bias

model.layers.21.input_layernorm.weight

model.layers.21.post_attention_layernorm.weight

model.layers.21.self_attn.q_proj.bias

model.layers.22.input_layernorm.weight

model.layers.22.post_attention_layernorm.weight

model.layers.22.self_attn.q_proj.bias

model.layers.23.input_layernorm.weight

model.layers.23.post_attention_layernorm.weight

model.layers.23.self_attn.q_proj.bias

model.layers.24.input_layernorm.weight

model.layers.24.post_attention_layernorm.weight

model.layers.24.self_attn.q_proj.bias

model.layers.25.input_layernorm.weight

model.layers.25.post_attention_layernorm.weight

model.layers.25.self_attn.q_proj.bias

model.layers.26.input_layernorm.weight

model.layers.26.post_attention_layernorm.weight

model.layers.26.self_attn.q_proj.bias

model.layers.27.input_layernorm.weight

model.layers.27.post_attention_layernorm.weight

model.layers.27.self_attn.q_proj.bias

model.layers.3.input_layernorm.weight

model.layers.3.post_attention_layernorm.weight

model.layers.3.self_attn.q_proj.bias

model.layers.4.input_layernorm.weight

model.layers.4.post_attention_layernorm.weight

model.layers.4.self_attn.q_proj.bias

model.layers.5.input_layernorm.weight

model.layers.5.post_attention_layernorm.weight

model.layers.5.self_attn.q_proj.bias

model.layers.6.input_layernorm.weight

model.layers.6.post_attention_layernorm.weight

model.layers.6.self_attn.q_proj.bias

model.layers.7.input_layernorm.weight

model.layers.7.post_attention_layernorm.weight

model.layers.7.self_attn.q_proj.bias

model.layers.8.input_layernorm.weight

model.layers.8.post_attention_layernorm.weight

model.layers.8.self_attn.q_proj.bias

model.layers.9.input_layernorm.weight

model.layers.9.post_attention_layernorm.weight

model.layers.9.self_attn.q_proj.bias

model.norm.weight

model.onnx ONNX

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onnx__MatMul_8769

onnx__MatMul_8770

onnx__MatMul_8771

onnx__MatMul_8772

onnx__MatMul_8773

onnx__MatMul_8780

onnx__MatMul_8781

onnx__MatMul_8804

onnx__MatMul_8805

onnx__MatMul_8806

onnx__MatMul_8807

onnx__MatMul_8808

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onnx__MatMul_8840

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onnx__MatMul_8843

onnx__MatMul_8850

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onnx__MatMul_8875

onnx__MatMul_8876

onnx__MatMul_8877

onnx__MatMul_8878

onnx__MatMul_8885

onnx__MatMul_8886

onnx__MatMul_8909

onnx__MatMul_8910

onnx__MatMul_8911

onnx__MatMul_8912

onnx__MatMul_8913

onnx__MatMul_8920

onnx__MatMul_8921

onnx__MatMul_8944

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onnx__MatMul_8946

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onnx__MatMul_9119

onnx__MatMul_9120

onnx__MatMul_9121

onnx__MatMul_9122

onnx__MatMul_9123

special_tokens_map.json

tokenizer.json

tokenizer_config.json