supra-nexus-o1-instruct

supra-nexus-o1-instruct - Qwen3-4B-2507 Based Model

Advanced instruction-following model based on Qwen3-4B-2507 (July 2025 version).

Model Specifications

• Architecture: Qwen3-4B-2507 (Latest July 2025 Release)
• Base Model: Qwen/Qwen3-4B-2507
• Parameters: 4,022,458,880 (4.02B)
• Hidden Size: 2560
• Layers: 36
• Attention Heads: 32
• KV Heads: 8 (GQA with 4:1 compression)
• Context Length: 262,144 tokens
• Vocabulary Size: 151,936

Performance Benchmarks

Official Qwen3-4B-2507 baseline performance with our enhancements:

Improvements due to chain-of-thought training and reasoning enhancements

Model Sizes

• FP16: ~8.04 GB
• INT8: ~4.02 GB (Quantized)
• INT4: ~2.01 GB (Aggressive Quantization)
• GGUF Q5_K_M: ~2.8 GB (Recommended for llama.cpp)

Key Features

• ✨ Based on latest Qwen3-4B-2507 (July 2025) improvements
• 🧠 Transparent reasoning with <thinking> tags
• 📈 Enhanced performance over base model
• 🚀 Optimized for production deployment
• 🔧 Multiple format support (GGUF, MLX, SafeTensors)

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Supra-Nexus/supra-nexus-o1-instruct")
tokenizer = AutoTokenizer.from_pretrained("Supra-Nexus/supra-nexus-o1-instruct")

# Example usage
messages = [{"role": "user", "content": "Explain quantum computing"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

With vLLM

from vllm import LLM, SamplingParams

llm = LLM(model="Supra-Nexus/supra-nexus-o1-instruct")
sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=512)

prompts = ["Explain the theory of relativity"]
outputs = llm.generate(prompts, sampling_params)

Training Details

• Base Model: Qwen3-4B-2507 (July 2025 release)
• Fine-tuning: LoRA with r=64, alpha=128
• Dataset: Custom reasoning dataset with CoT examples
• Training Framework: Zoo Gym
• Hardware: NVIDIA A100 GPUs

Links

• 🤗 Model Collection
• 📊 Training Dataset
• 💻 GitHub Repository
• 📄 Research Paper

Citation

@software{supra_nexus_o1_2025,
  title = {Supra Nexus O1: Transparent Reasoning with Qwen3-4B-2507},
  author = {Supra Foundation},
  year = {2025},
  month = {September},
  url = {https://github.com/Supra-Nexus/o1},
  note = {Based on Qwen3-4B-2507 (July 2025)}
}

License

Apache 2.0 - Commercial use permitted

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Built on Qwen3-4B-2507 - The July 2025 milestone in open language models