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import requests
import re
url = "https://api.siliconflow.cn/v1/models"
visual_model_list = [
"Qwen/Qwen2-VL-72B-Instruct",
"OpenGVLab/InternVL2-26B",
"TeleAI/TeleMM",
"Pro/Qwen/Qwen2-VL-7B-Instruct",
"Pro/OpenGVLab/InternVL2-8B"
]
reasoning_model_list = [
"Qwen/QwQ-32B-Preview",
"Qwen/QVQ-72B-Preview",
"AIDC-AI/Marco-o1"
]
excluded_models = [
"deepseek-ai/deepseek-vl2",
"01-ai/Yi-1.5-6B-Chat"
]
image_model_list = [
"black-forest-labs/FLUX.1-dev",
"stabilityai/stable-diffusion-3-5-large"
]
qwen_pattern = re.compile(r'^Qwen/')
meta_llama_pattern = re.compile(r'^meta-llama/')
deepseek_ai_pattern = re.compile(r'^deepseek-ai/')
pro_lora_pattern = re.compile(r'^(Pro|LoRA)/')
def extract_version_and_params(model):
version_match = re.search(r'(\d+(\.\d+)+)', model)
version = float(version_match.group(1)) if version_match else 0.0
params_match = re.search(r'(\d+(\.\d+)?)(B|b)', model)
params = float(params_match.group(1)) if params_match else 0.0
return version, params
def sort_models(model_list):
return sorted(model_list, key=lambda x: extract_version_and_params(x), reverse=True)
def text_model(api_key: str) -> list:
model_list = []
querystring = {"type":"text","sub_type":"chat"}
headers = {"Authorization": f"Bearer {api_key}"}
response = requests.request("GET", url, params=querystring, headers=headers)
if response.status_code == 200:
response_object = response.json()
response_data = response_object["data"]
for i in response_data:
if i["id"] not in visual_model_list and i["id"] not in reasoning_model_list and i["id"] not in excluded_models:
model_list.append(i["id"])
qwen_models = [model for model in model_list if qwen_pattern.search(model) and not pro_lora_pattern.search(model)]
meta_llama_models = [model for model in model_list if meta_llama_pattern.search(model) and not pro_lora_pattern.search(model)]
deepseek_ai_models = [model for model in model_list if deepseek_ai_pattern.search(model) and not pro_lora_pattern.search(model)]
other_models = [model for model in model_list if not qwen_pattern.search(model) and not meta_llama_pattern.search(model) and not deepseek_ai_pattern.search(model) and not pro_lora_pattern.search(model)]
pro_lora_models = [model for model in model_list if pro_lora_pattern.search(model)]
qwen_models_sorted = sort_models(qwen_models)
meta_llama_models_sorted = sort_models(meta_llama_models)
deepseek_ai_models_sorted = sort_models(deepseek_ai_models)
other_models_sorted = sort_models(other_models)
pro_lora_models_sorted = sort_models(pro_lora_models)
model_list = qwen_models_sorted + meta_llama_models_sorted + deepseek_ai_models_sorted + other_models_sorted + pro_lora_models_sorted
return model_list
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