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Model: applexml/kimi-k2 Source: Original Platform
2026-06-01 01:32:27 +08:00
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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"],
+  },
+}
+```