Model Card for Qwen3Fangwusha32B
Qwen3Fangwusha32B is a 32B-parameter large language model fine-tuned from Qwen3-32B, optimized for high-performance Chinese natural language understanding, generation, long-text reasoning, and complex task execution.
Model Details
Model Description
This model is a heavyweight Chinese large language model built on the Qwen3-32B base architecture. It is fine-tuned to enhance instruction following, logical reasoning, long document processing, and professional content generation capabilities for industrial and advanced research scenarios.
- Developed by: Yougen Yuan
- Funded by [optional]: Personal Research Project
- Shared by [optional]: Yougen Yuan
- Model type: Decoder-only Large Language Model
- Language(s) (NLP): Chinese (Simplified)
- License: Apache-2.0
- Finetuned from model [optional]: Qwen3-32B
Model Sources [optional]
- Repository: https://huggingface.co/Yougen/Qwen3Fangwusha32B
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This model can be directly used for:
- Complex Chinese instruction following and task execution
- Long-text understanding, summarization, and analysis
- Professional content generation and writing assistance
- Advanced dialogue and multi-turn question answering
- Logical reasoning, planning, and structured output generation
Downstream Use [optional]
Can be further fine-tuned for:
- Enterprise-level intelligent question answering systems
- Domain-specific large model applications (legal, financial, technical)
- High-performance RAG systems with long-context support
- Automated document processing and report generation
- AI agents and tool-using systems
Out-of-Scope Use
- Not intended for unregulated high-stakes decision-making (medical, legal, financial without review)
- Not suitable for generating harmful, illegal, misleading, or privacy-violating content
- Not optimized for non-Chinese languages
- Not designed for edge or low-resource devices due to its large parameter size
Bias, Risks, and Limitations
- The model may inherit social, cultural, and factual biases from the pre-training data of the base Qwen3 model.
- Although capable of complex reasoning, it may still produce hallucinations or factually incorrect content.
- Performance may vary across highly specialized domains without further domain adaptation.
- Long-text inputs may exceed context window limits and cause degradation in coherence.
Recommendations
All outputs used in professional or production environments should be reviewed by humans.
Content safety and fact-checking modules are strongly recommended for public deployment.
Users should ensure compliance with local laws and ethical guidelines before application.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
How to Get Started with the Model
Use the code below to load and inference with the model:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model_name = "Yougen/Qwen3Fangwusha32B"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
6
7prompt = "详细分析大模型在企业知识库中的应用方案"
8inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
9outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.95)
10print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
Training data consists of high-quality Chinese instruction-following corpora, long documents, professional domain texts, and multi-turn dialogue data. All data is processed with deduplication, noise filtering, and quality control.
Training Procedure
Preprocessing [optional]
- Text cleaning and normalization
- Instruction template formatting for multi-task learning
- Long-sequence tokenization with appropriate truncation and padding
Training Hyperparameters
- Training regime: bf16 mixed precision
- Learning rate: 1.5e-5
- Batch size: 8
- Optimizer: AdamW
- Weight decay: 0.01
- Epochs: 2
Speeds, Sizes, Times [optional]
- Model parameter size: 32B
- Training hardware: NVIDIA A100 / H100 GPU clusters
- Training duration: Multiple days
Evaluation
Testing Data, Factors & Metrics
Testing Data
Internal Chinese benchmark set covering reasoning, instruction following, long-text understanding, and generation quality.
Factors
Context length, domain complexity, reasoning difficulty, multi-turn interaction quality.
Metrics
- Perplexity
- BLEU / ROUGE
- Human evaluation (fluency, rationality, accuracy)
- Instruction compliance rate
Results
[More Information Needed]
Summary
The model achieves strong performance in complex Chinese understanding and reasoning tasks, suitable for high-demand industrial and research scenarios.
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: NVIDIA A100 / H100
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
Decoder-only transformer architecture based on Qwen3-32B.
Optimized for strong Chinese reasoning, long-text modeling, and high-quality natural language generation.
Compute Infrastructure
Hardware
NVIDIA GPU cluster with NVLink support
Software
- PyTorch
- Hugging Face Transformers & Accelerate
- FlashAttention
- Datasets & Tokenizers
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
- Decoder-only LLM: Autoregressive language model using only transformer decoder layers.
- Fine-tuning: Process of adapting a pre-trained model to downstream tasks.
- Qwen3: High-performance large language model series developed by Alibaba Cloud.
More Information [optional]
For updates, issues, or usage questions, please refer to the model repository on the Hugging Face Hub.
Model Card Authors [optional]
Yougen Yuan
Model Card Contact
[More Information Needed]
