license: apache-2.0
base_model:
- Qwen/QwQ-32B
pipeline_tag: text-classification
language:
- en
library_name: transformers
tags:
- chat
TransMind - ้้ธฃๆบๅ
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 67.1B parameters, 37B activated).
Key Features
๐ 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
โก 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
๐ ๏ธ 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
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:
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