yasserrmd/qwen3-4b-agentic-reasoner

Qwen3-4B-Agentic-Reasoner

<img src="modelcard.png" widhth="400"/>

yasserrmd/qwen3-4b-agentic-reasoner is a merged model that combines the agentic instruction-following strength of Menlo/Jan-nano with the reasoning and structured thought capabilities of POLARIS-Project/Polaris-4B-Preview, using the Qwen/Qwen3-4B architecture as the base.

This merge was performed using mergekit and the TIES method for fine-grained parameter blending.

🧠 Intended Use

This model is intended for use in:

• —Multi-step reasoning tasks
• —Agent-style instruction following (CLI assistants, web automation)
• —Educational assistance, planning, and explanation
• —Natural language code generation, JSON/schema design
• —Legal, productivity, and roleplay simulations

🧪 Merge Details

🔀 Merge Method

This model was merged using the TIES merge method with the Qwen/Qwen3-4B as the base model.

🤝 Models Merged

⚙️ Configuration

models:
  - model: POLARIS-Project/Polaris-4B-Preview
    parameters:
      weight: 0.5
  - model: Menlo/Jan-nano
    parameters:
      weight: 0.5
merge_method: ties
base_model: Qwen/Qwen3-4B
parameters:
  normalize: true
  int8_mask: true
dtype: float16

📊 Prompt Evaluation

This model was evaluated on handcrafted prompts covering:

• —Chain-of-thought reasoning
• —Math and logic
• —Code writing and CLI instructions
• —JSON/schema generation
• —Role-based planning and writing tasks
• —Arabic translation
• —Legal drafting

✅ Performance Highlights

Average prompt score (0–3 scale): 2.15 All outputs were logical, well-structured, and contextually accurate for the prompt types.

🚀 Inference

To use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "yasserrmd/qwen3-4b-agentic-reasoner"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True)

prompt = "Plan the first 3 steps for launching a nonprofit AI education platform."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚠️ License & Use

Respect the licenses of the original merged models. This model is released for research and personal experimentation purposes.

• —POLARIS-Project/Polaris-4B-Preview License
• —Menlo/Jan-nano License
• —Qwen3-4B License

🙏 Acknowledgments

Thanks to the teams behind:

• —Alibaba's Qwen3 series
• —Menlo/Jan-nano project
• —POLARIS RL framework
• —MergeKit by @cg123

Model by @yasserrmd