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Skywork/
MindLink-32B-0801

Name: MindLink-32B-0801
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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

🤖 Model	📏 Context Length	⬇️ Download
MindLink 32B	`128K`	🤗 HuggingFace
MindLink 72B	`128K`	🤗 HuggingFace

📖 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.

python

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:
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`:

bash

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:

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:

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.

Skywork/MindLink-32B-0801