MindLink-32B-0801

MindLink#

English | 中文

Model Description#

We introduce MindLink, a new family of large language models developed by Kunlun Inc. Built on Qwen, these models incorporate our latest advances in post-training techniques. MindLink demonstrates strong performance across various common benchmarks and is widely applicable in diverse AI scenarios. We welcome feedback to help us continuously optimize and improve our models.

🚀 Model Downloads#

📖 Technical Report#

Our training methodology and evaluation: MindLink

Highlights#

• Plan-based Reasoning: Without the "think" tag, MindLink achieves competitive performance with leading proprietary models across a wide range of reasoning and general tasks. It significantly reduces inference cost, and improves multi-turn capabilities.
• Mathematical Framework: It analyzes the effectiveness of both Chain-of-Thought (CoT) and Plan-based Reasoning.
• Adaptive Reasoning: it automatically adapts its reasoning strategy based on task complexity: complex tasks produce detailed reasoning traces, while simpler tasks yield concise outputs.

Quickstart#

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

⚠️ Please make sure you have installed transformers>=4.51.0. Lower versions are not supported.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Skywork/MindLink-32B-0801"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "What is the capital of China?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

For deployment, you can use sglang>=0.4.6.post1 to create an OpenAI-compatible API endpoint:

• SGLang: shell python -m sglang.launch_server --model-path Skywork/MindLink-32B-0801

API Access#

📢 We provide developers with a one-month free trial of our API for exploring and testing our models. To request access to an Open WebUI account (https://sd1svahsfo0m61h76e190.apigateway-cn-beijing.volceapi.com), please contact us at: mindlink@skywork.ai

⚠️ Note: If you encounter inconsistent responses during inference, we recommend clearing the session context (history) and retrying.

🔧 Usage Instructions#

Our Chat API supports OpenAI's format. Simply include your API Key with HTTP POST requests.

✅ Sample Request using curl:#

curl -X POST https://sd2690u280c6ft26qcdi0.apigateway-cn-beijing.volceapi.com/v1/chat/completions \
     -H "Authorization: Bearer nc6Dt7DrLJNzLELiqOR1bogO5Oh1qHtO" \
     -H "Content-Type: application/json" \
     -d '{
           "model": "Mind_Link_beta_32B",
           "messages": [
             {"role": "user", "content": "What is the capital of China?"}
           ],
           "temperature": 0.7,
           "max_tokens": 128,
           "stream": false
         }'

🐍 Sample Request using Python:#

import requests

API_KEY = "nc6Dt7DrLJNzLELiqOR1bogO5Oh1qHtO"
API_URL = "https://sd2690u280c6ft26qcdi0.apigateway-cn-beijing.volceapi.com/v1/chat/completions"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

payload = {
    "model": "Mind_Link_beta_32B",
    "messages": [
        {"role": "user", "content": "What is the capital of China?"}
    ],
    "temperature": 0.7,
    "max_tokens": 128,
    "stream": False
}

response = requests.post(API_URL, headers=headers, json=payload)

if response.status_code == 200:
    reply = response.json()
    print("MindLink Response:")
    print(reply["choices"][0]["message"]["content"])
else:
    print(f"Error {response.status_code}: {response.text}")

🌐 API Interface Details#

• Endpoint: https://sd2690u280c6ft26qcdi0.apigateway-cn-beijing.volceapi.com/v1/chat/completions
• Authentication: Use your API key via Authorization: Bearer <api_key>
• Request Format: Compatible with OpenAI's Chat Completion API
• Supported Fields: model, messages, temperature, top_p, max_tokens, stream, stop, etc.
• Model Identifiers: Use either "Mind_Link_beta_32B" or "Mind_Link_beta_72B"
• Public API Key: We provide the following public API key: "nc6Dt7DrLJNzLELiqOR1bogO5Oh1qHtO" (requests via this key enter a queue and have limited request rates; contact us for unlimited access).

Evaluation#

The results are shown below: Image: Comparison between MindLink (ML) and other frontier models across various benchmarks.

License and Usage Information#

Model License and Terms of Use#

1. Core License#

This model is licensed under the Apache License 2.0, granting users the following rights:

✅ Commercial deployment

✅ Source code modification

✅ Patent authorization

✅ Closed-source derivatives

⚠️ Prohibition on using model names/logos for promotion without written authorization

⚠️ No warranties provided

2. Inheritance Declaration#

This model is based on improvements from Qwen (Apache 2.0 License). You must:

• Retain original Qwen copyright notices in derivative works.
• Clearly document changes made in modification notes.
• Adhere to any additional usage restrictions imposed by Qwen.

If you have any questions, please raise an issue or contact us at mindlink@skywork.ai.