Instructions to use yasserrmd/AgenticCoder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/AgenticCoder-4B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/AgenticCoder-4B")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/AgenticCoder-4B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yasserrmd/AgenticCoder-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/AgenticCoder-4B"
# 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/AgenticCoder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/AgenticCoder-4B

SGLang

How to use yasserrmd/AgenticCoder-4B 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/AgenticCoder-4B" \
    --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/AgenticCoder-4B",
		"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/AgenticCoder-4B" \
        --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/AgenticCoder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yasserrmd/AgenticCoder-4B with Docker Model Runner:
```
docker model run hf.co/yasserrmd/AgenticCoder-4B
```

yasserrmd commited on Jul 29, 2025

Commit

26aed62

verified ·

1 Parent(s): 58b225e

Update README.md

Browse files

Files changed (1) hide show

README.md +64 -21

README.md CHANGED Viewed

@@ -8,38 +8,81 @@ tags:
 - merge
 ---
-# merged-medical-reasoning
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
-### Models Merged
-The following models were included in the merge:
-* [ertghiu256/qwen3-4b-code-reasoning](https://huggingface.co/ertghiu256/qwen3-4b-code-reasoning)
-* [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-merge_method: slerp
-base_model: Menlo/Jan-nano
-models:
-  - model: Menlo/Jan-nano
-  - model: ertghiu256/qwen3-4b-code-reasoning
-parameters:
-  t: 0.4   # 70% base (MedScholar), 30% Nemotron reasoning
-dtype: float16
-tokenizer_source: Menlo/Jan-nano
 ```

 - merge
 ---
+# 🧠 AgenticCoder‑4B
+**AgenticCoder‑4B** is a compact 4B parameter language model designed for autonomous agent workflows and intelligent code reasoning. It merges the planning and tool-use strengths of `Jan-nano` with the coding and logic capabilities of `Qwen3‑4B‑Code‑Reasoning`, creating a balanced model ideal for real-world assistant scenarios, research agents, and smart development tools.
+---
+## ✨ Key Features
+- 🔁 **Agentic Planning & MCP Alignment**
+  Trained on datasets and architectures optimized for multi-step reasoning, task decomposition, and memory–contextual workflows.
+- 💻 **Code Understanding & Reasoning**
+  Strong capabilities in Python code generation, script explanation, optimization, and multi-turn task development.
+- 🧰 **Tool Use Simulation**
+  Handles realistic tool interaction prompts such as CSV analysis, OCR, and file parsing in code.
+- 📦 **Compact & Efficient (4B)**
+  Lightweight enough for cost-efficient deployment, edge device integration, and fine-tuning.
+---
+## 🛠️ Merge Details
+- **Merge Method:** SLERP (`t = 0.4`)
+- **Base Model:** [`Menlo/Jan-nano`](https://huggingface.co/Menlo/Jan-nano)
+- **Merged With:** [`ertghiu256/qwen3-4b-code-reasoning`](https://huggingface.co/ertghiu256/qwen3-4b-code-reasoning)
+- **Precision:** `float16`
+- **Tokenizer Source:** `Menlo/Jan-nano`
+---
+## 📎 Example Use Cases
+```text
+✅ "Design a 3-week beginner Python curriculum including AI tools."
+✅ "Write a Python function to recursively scan JSON for a key, without using recursion."
+✅ "Read a folder of images and extract text using OCR, save to files."
+✅ "Summarize trends in a sales CSV and visualize monthly performance."
+````
+---
+## 📁 License & Use
+This model is provided for research and development use under the terms of the base models’ respective licenses. Please ensure compliance before commercial usage.
+---
+## 🧬 Citation
+If you use this model, consider citing it as:
 ```
+@misc{agenticcoder4b2025,
+  title={AgenticCoder-4B: A Compact Agent + Code Reasoning Model},
+  author={Yasser, M.},
+  year={2025},
+  url={https://huggingface.co/your-username/AgenticCoder-4B}
+}
+```
+---
+## 🤝 Acknowledgements
+* [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano) by Menlo Systems
+* [Qwen3‑4B‑Code‑Reasoning](https://huggingface.co/ertghiu256/qwen3-4b-code-reasoning) by ertghiu256
+* MergeKit, SLERP, Hugging Face
+---