TransMind is an expert AI model for the communications domain, built on an advanced large language model architecture and specifically optimized for the telecommunications industry. Developed on the robust QwQ-32B foundation, this model achieves deep integration of communication knowledge and enhanced professional capabilities through domain-specific reinforcement learning. With 32 billion parameters, its performance rivals DeepSeek-R1 (which utilizes 671B parameters, 37B activated).

Key Features

🚀 Expert-Level Communication Capabilities

Mastery of communication protocols (5G/6G, TCP/IP, HTTP/3); Profound understanding of wireless communication principles & signal processing; Network optimization & fault diagnosis expertise; Communication system design & planning proficiency; Professional interpretation of telecom standards & specifications

⚡ Reinforcement Learning Enhanced Architecture

Powerful 32B-parameter foundation based on QwQ-32B; Optimized communication-domain reasoning via large-scale RL; Multi-phase training integrating specialized communication data; Deep reasoning for complex communication problem-solving; Domain-specific reward functions (Technical accuracy/Solution feasibility/Efficiency optimization/Innovation); Adaptive learning with dynamic strategy adjustment

🛠️ Intelligent Agent Capabilities

Integrated communication-specific tool support; Dynamic solution adjustment based on network feedback; End-to-end system analysis & optimization; Multi-step technical diagnosis & troubleshooting; Real-time performance monitoring & feedback loops

Technical Advantages

mermaid

graph LR
A[QwQ-32B Base Architecture] --> B[Communication-Domain RL]
B --> C[Protocol Expertise]
B --> D[Network Optimization Engine]
B --> E[System Design Capabilities]
C --> F[TransMind]

Quick Start

Example using apply_chat_template to load tokenizer/model and generate content:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/QwQ-32B"

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

prompt = "How many r's are in the word \"strawberry\""
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=32768
)
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]
print(response)

Contribution & Licensing

We welcome communication domain experts to participate in model optimization! Contribute through:

Submitting specialized communication datasets

Reporting domain-specific issues

Optimizing communication tool integrations

License: Apache License 2.0

Hanoch4869/TransMind-32B-GGUF

TransMind - 通鸣智响