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README.md

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----
-license: apache-2.0
----
+---
+library_name: rkllm
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Math-7B-Instruct
+tags:
+  - rkllm
+  - rk3588
+  - rockchip
+  - edge-ai
+  - llm
+  - math
+  - chat
+---
+Qwen2.5-Math-7B-Instruct — RKLLM build for RK3588 boards
+
+**Author:** @jamescallander  
+**Source model:** [Qwen/Qwen2.5-Math-7B-Instruct · Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct)
+
+> This repository hosts a **conversion** of `Qwen2-Math-7B-Instruct` for use on Rockchip RK3588 single-board computers (Orange Pi 5 plus, Radxa Rock 5b+, Banana Pi M7, etc.). Conversion was performed using the [RKNN-LLM toolkit](https://github.com/airockchip/rknn-llm?utm_source=chatgpt.com)
+
+#### Conversion details
+
+- RKLLM-Toolkit version: v1.2.1
+  
+- NPU driver: v0.9.8
+  
+- Python: 3.12
+  
+- Quantization: `w8a8_g128`
+  
+- Output: single-file `.rkllm` artifact
+  
+- Tokenizer: not required at runtime (UI handles prompt I/O)
+  
+
+## ⚠️ Math reasoning disclaimer
+
+🛑 **This model may make calculation or reasoning errors.**
+
+- It is intended for **educational and experimental purposes only**.
+  
+- Always **double-check results** with trusted methods, calculators, or domain experts.
+  
+- Outputs should not be used as the sole basis for academic, financial, or scientific decisions.
+  
+- Use responsibly and verify correctness before relying on results.
+  
+
+## Intended use
+
+- On-device math reasoning and step-by-step problem solving.
+  
+- Qwen2.5-Math-7B-Instruct is tuned for **mathematics and quantitative reasoning tasks** (problem solving, proofs, step-by-step derivations).
+  
+
+## Limitations
+
+- Requires 8GB free memory
+  
+- Quantized build (`w8a8_g128`) may show small quality differences vs. full-precision upstream.
+  
+- Tested on Radxa Rock 5B+; other devices may require different drivers/toolkit versions.
+  
+- Generated code should always be reviewed before use in production systems.
+  
+
+## Quick start (RK3588)
+
+### 1) Install runtime
+
+The RKNN-LLM toolkit and instructions can be found on the specific development board's manufacturer website or from [airockchip's github page](https://github.com/airockchip).
+
+Download and install the required packages as per the toolkit's instructions.
+
+### 2) Simple Flask server deployment
+
+The simplest way the deploy the `.rkllm` converted model is using an example script provided in the toolkit in this directory: `rknn-llm/examples/rkllm_server_demo`
+
+```bash
+python3 <TOOLKIT_PATH>/rknn-llm/examples/rkllm_server_demo/flask_server.py \
+  --rkllm_model_path <MODEL_PATH>/Qwen2.5-Math-7B-Instruct_w8a8_g128_rk3588.rkllm \
+  --target_platform rk3588
+```
+
+### 3) Sending a request
+
+A basic format for message request is:
+
+```json
+{
+    "model":"Qwen2.5-Math-7B-Instruct",
+    "messages":[{
+        "role":"user",
+        "content":"<YOUR_PROMPT_HERE>"}],
+    "stream":false
+}
+```
+
+Example request using `curl`:
+
+```bash
+curl -s -X POST <SERVER_IP_ADDRESS>:8080/rkllm_chat \
+    -H 'Content-Type: application/json' \
+    -d '{"model":"Qwen2.5-Math-7B-Instruct","messages":[{"role":"user","content":"How is sample standard deviation calculated?"}],"stream":false}'
+```
+
+The response is formated in the following way:
+
+```json
+{
+    "choices":[{
+        "finish_reason":"stop",
+        "index":0,
+        "logprobs":null,
+        "message":{
+            "content":"<MODEL_REPLY_HERE">,
+            "role":"assistant"}}],
+        "created":null,
+        "id":"rkllm_chat",
+        "object":"rkllm_chat",
+        "usage":{
+            "completion_tokens":null,
+            "prompt_tokens":null,
+            "total_tokens":null}
+}
+```
+
+Example response:
+
+```json
+{"choices":[{"finish_reason":"stop","index":0,"logprobs":null,"message":{"content":"To calculate the sample standard deviation, follow these steps: 1. **Calculate the mean (average) of the sample:** \[ \bar{x} = \frac{\sum_{i=1}^{n} x_i}{n} \] where \( x_i \) are the individual data points and \( n \) is the number of data points. 2. **Calculate the squared differences from the mean for each data point:** \[ (x_i - \bar{x})^2 \] 3. **Sum the squared differences:** \[ \sum_{i=1}^{n} (x_i - \bar{x})^2 \] 4. **Divide the sum of the squared differences by \( n-1 \) (this is called the Bessel's correction):** \[ s^2 = \frac{\sum_{i=1}^{n} (x_i - \bar{x})^2}{n-1} \] where \( s^2 \) is the sample variance. 5. **Take the square root of the sample variance to get the sample standard deviation:** \[ s = \sqrt{s^2} = \sqrt{\frac{\sum_{i=1}^{n} (x_i - \bar{x})^2}{n-1}} \] So, the formula for the sample standard deviation is: \[ \boxed{s = \sqrt{\frac{\sum_{i=1}^{n} (x_i - \bar{x})^2}{n-1}}} \]","role":"assistant"}}],"created":null,"id":"rkllm_chat","object":"rkllm_chat","usage":{"completion_tokens":null,"prompt_tokens":null,"total_tokens":null}}
+```
+
+### 4) UI compatibility
+
+This server exposes an **OpenAI-compatible Chat Completions API**.
+
+You can connect it to any OpenAI-compatible client or UI (for example: [Open WebUI](https://github.com/open-webui/open-webui?utm_source=chatgpt.com))
+
+- Configure your client with the API base: `http://<SERVER_IP_ADDRESS>:8080` and use the endpoint: `/rkllm_chat`
+- Make sure the `model` field matches the converted model’s name, for example:
+
+```json
+{
+ "model": "Qwen2.5-Math-7B-Instruct",
+ "messages": [{"role":"user","content":"Hello!"}],
+ "stream": false
+}
+```
+
+# License
+
+This conversion follows the license of the source model: [LICENSE · Qwen/Qwen2.5-Math-7B-Instruct at main](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE)