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language:
- en
license: apache-2.0
tags:
- llm
- tool-calling
- lightweight
- agentic-tasks
- react
- mlx
model-index:
- name: NanoAgent
results: []
datasets:
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- allenai/tulu-3-sft-personas-instruction-following
- xingyaoww/code-act
- m-a-p/Code-Feedback
- weijie210/gsm8k_decomposed
- Locutusque/function-calling-chatml
- HuggingFaceTB/smoltalk
base_model:
- HuggingFaceTB/SmolLM2-135M-Instruct
pipeline_tag: text-generation
---
# POC
# FORKED FROM
# 🧠 NanoAgent — 135M Parameter Agentic LLM
NanoAgent is a compact 135M parameter, 8k context-length language model trained to **perform tool calls** and **generate responses based on tool outputs**.
Despite its small size (~135 MB in 8-bit precision), it’s optimized for agentic use cases and runs easily on personal devices.
**Github:** [NanoAgent](https://github.com/QuwsarOhi/NanoAgent)
**Inference resource:** [link](https://github.com/QuwsarOhi/NanoAgent/blob/main/notebooks/inference.ipynb)
---
## ✨ Features
- 🧰 **Tool Calling** — understands and responds with structured outputs from tool calls.
- 🧭 **Instruction Following** — strong instruction following abilities.
- 🧠 **Basic Reasoning** — handles lightweight reasoning and ReAct-style interactions.
- ⚡ **Lightweight** — runs on local hardware with minimal resources.
---
## 🧪 Training Overview
**Base model:** [`SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
**Fine-tuning method:** [Dynamic Fine-Tuning (DFT)](https://github.com/yongliang-wu/DFT/tree/master)
**Hardware:** Apple Mac M1 (16 GB Unified Memory) using MLX.
### 📚 Datasets Used
- `microsoft/orca-agentinstruct-1M-v1` — agentic tasks, RAG answers, classification
- `microsoft/orca-math-word-problems-200k` — lightweight reasoning
- `allenai/tulu-3-sft-personas-instruction-following` — instruction following
- `xingyaoww/code-act` — ReAct style reasoning and action
- `m-a-p/Code-Feedback` — alignment via feedback
- `HuggingFaceTB/smoltalk` + `/apigen` — tool calling stabilization
- `weijie210/gsm8k_decomposed` — question decomposition
- `Locutusque/function-calling-chatml` — tool call response structure
---
## ⚠️ Disclaimer
This is a **beta model**.
- It may produce **incorrect** or **incomplete** outputs.
- Tool call execution is **basic** and can fail in some cases.
- Intended for **research and experimentation** only — not production use.
---
## 🧭 Roadmap
- ✅ Initial release with DFT fine-tuning
- 🧪 Benchmarking on agentic tasks
- ~~🔬 Experimenting with GRPO for tool calling (failed)~~
- 🧠 Weight merging experiments for improved performance
- Add more tool calling dataset
---
## 📥 Model Size
- 135M parameters
- ~135 MB in 8-bit precision
- 8k context length
---
## ⚡ Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "quwsarohi/NanoAgent-135M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def inference(messages, max_new_tokens=256, temperature=0.3, min_p=0.15, **kwargs):
input_text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(
inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
min_p=0.15,
temperature=temperature,
**kwargs
)
return tokenizer.decode(outputs[0][inputs.shape[1] :], skip_special_tokens=True)
messages = [{"role": "user", "content": "Hi! Do you have a name?"}]
print(inference(messages))
```
Use the following template for tool calling:
```python
TOOL_TEMPLATE = """You are a helpful AI assistant. You have a set of possible functions/tools inside <tools></tools> tags.
Based on question, you may need to make one or more function/tool calls to answer user.
You have access to the following tools/functions:
<tools>{tools}</tools>
For each function call, return a JSON list object with function name and arguments within <tool_call></tool_call> tags."""
```
Sample tool call definition:
```json
{
"name": "web_search",
"description": "Performs a web search for a query and returns a string of the top search results formatted as markdown with titles, links, and descriptions.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query to perform.",
}
},
"required": ["query"],
},
}
``` |