Instructions to use yasserrmd/qwen3-4b-agentic-reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/qwen3-4b-agentic-reasoner with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yasserrmd/qwen3-4b-agentic-reasoner")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/qwen3-4b-agentic-reasoner")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/qwen3-4b-agentic-reasoner")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yasserrmd/qwen3-4b-agentic-reasoner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/qwen3-4b-agentic-reasoner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/qwen3-4b-agentic-reasoner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/qwen3-4b-agentic-reasoner

SGLang

How to use yasserrmd/qwen3-4b-agentic-reasoner with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yasserrmd/qwen3-4b-agentic-reasoner" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/qwen3-4b-agentic-reasoner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yasserrmd/qwen3-4b-agentic-reasoner" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/qwen3-4b-agentic-reasoner",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yasserrmd/qwen3-4b-agentic-reasoner with Docker Model Runner:
```
docker model run hf.co/yasserrmd/qwen3-4b-agentic-reasoner
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B-Agentic-Reasoner

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

Model	Role
POLARIS-Project/Polaris-4B-Preview	Deep reasoning & CoT
Menlo/Jan-nano	Agentic & Instruction-following

⚙️ 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

Criterion	Result
CoT Reasoning	Excellent (multi-step math, planning)
Agentic Tasks	Strong (shell scripts, terminal agents)
Code Output	Clean formatting and logical structure
Format Awareness	Recognizes JSON, email, legal structure
Instruction Follow-through	Reliable and contextual
Language Tasks	Accurate Arabic translation, paraphrase

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))