README.md

---
library_name: rkllm
license: other
license_name: deepseek
license_link: >-
  https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct/blob/main/LICENSE
language:
- en
base_model:
- deepseek-ai/deepseek-coder-6.7b-instruct
pipeline_tag: text-generation
tags:
  - rkllm
  - rk3588
  - rockchip
  - code
  - edge-ai
  - llm
---
# deepseek-coder-6.7b-instruct — RKLLM build for RK3588 boards

**Author:** @jamescallander  
**Source model:** [meta-llama/CodeLlama-7b-Instruct-hf · Hugging Face](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf)  
**Target:** Rockchip RK3588 NPU via RKNN-LLM Runtime

> This repository hosts a **conversion** of `deepseek-coder-6.7b-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)

## ⚠️ Code generation disclaimer

🛑 **This model may produce incorrect or insecure code.**

- It is intended for **research, educational, and experimental purposes only**.
- Always **review, test, and validate code outputs** before using them in real projects.
- Do not rely on outputs for production, security-sensitive, or safety-critical systems.
- Use responsibly and in compliance with the source model’s license and restrictions.

## Intended use

- On-device coding assistant / code generation on RK3588 SBCs.
- deepseek-coder-6.7b-instruct is tuned for software development and programming tasks, making it suitable for **edge deployment** where privacy and low power use are priorities.

## Limitations

- Requires 9.1GB 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>/deepseek-coder-6.7b-instruct_w8a8_g128_rk3588.rkllm \
  --target_platform rk3588
```

### 3) Sending a request

A basic format for message request is:

```json
{
    "model":"deepseek-coder-6.7b-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":"CodeLlama-7b-Instruct-hf","messages":[{"role":"user","content":"Create a python function to calculate factorials using recursive method."}],"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":"Sure, here is the Python code for calculating factorial of a number using recursion: ```python def factorial(n): if n == 0 or n == 1: # base case return 1 else: return n * factorial(n-1) ``` This function works by repeatedly calling itself with the argument `n - 1`, until it reaches a point where `n` is either `0` or `1`. At this point, it returns `1` and the recursion ends. The product of all these returned values gives us the factorial of the original input number.","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": "deepseek-coder-6.7b-instruct",
 "messages": [{"role":"user","content":"Hello!"}],
 "stream": false
}
```

# License

This conversion follows the license of the source model: [LICENSE · deepseek-ai/deepseek-coder-6.7b-instruct at main](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct/blob/main/LICENSE)
- -**Required notice:** see [`NOTICE`](NOTICE)