Instructions to use whcl412/LycheeAI-coder-1.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use whcl412/LycheeAI-coder-1.7b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use whcl412/LycheeAI-coder-1.7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "whcl412/LycheeAI-coder-1.7b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "whcl412/LycheeAI-coder-1.7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Ollama
How to use whcl412/LycheeAI-coder-1.7b-GGUF with Ollama:
```
ollama run hf.co/whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
```

Unsloth Studio

How to use whcl412/LycheeAI-coder-1.7b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for whcl412/LycheeAI-coder-1.7b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for whcl412/LycheeAI-coder-1.7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for whcl412/LycheeAI-coder-1.7b-GGUF to start chatting

How to use whcl412/LycheeAI-coder-1.7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use whcl412/LycheeAI-coder-1.7b-GGUF with Docker Model Runner:
```
docker model run hf.co/whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M
```

Lemonade

How to use whcl412/LycheeAI-coder-1.7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LycheeAI-coder-1.7b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use whcl412/LycheeAI-coder-1.7b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use whcl412/LycheeAI-coder-1.7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "whcl412/LycheeAI-coder-1.7b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LycheeAI-coder-1.7b-GGUF

当前版本：V12（思考修复版）

LycheeAI-coder-1.7b 的 GGUF 量化版本，基于 Qwen3-1.7B 通过 QLoRA（4-bit）微调，代码优先、支持中/英/粤三语对话、情感共情、可开关思考。V12 重点修复了 GGUF 版思考退化问题。

本仓库提供 GGUF 格式（跨平台，Ollama / llama.cpp / LM Studio 通用）。如需 Apple Silicon 的 MLX 版本，见 mlx-LycheeAI-coder-1.7b。

可用文件

文件	大小	量化	说明
mlx-LycheeAI-coder-1.7b-q4_k_m.gguf	1.05GB	Q4_K_M	推荐，平衡质量与大小

使用方式

Ollama

# 1. 下载 q4_k_m 版本后，创建 Modelfile（或直接用仓库内 Modelfile）
ollama create lychee-coder -f Modelfile
# 2. 运行
ollama run lychee-coder "用 Python 写一个快速排序"

llama.cpp

llama-cli -m mlx-LycheeAI-coder-1.7b-q4_k_m.gguf -p "写一个二分查找" -n 256

训练信息

基座模型：Qwen3-1.7B
微调方法：QLoRA（4-bit + LoRA rank 16）
第一轮：CodeAlpaca-20k 代码指令 3500 + 中文 800 + 英语 800 + 粤语 600 + 身份问答 24（共 5724 条，lr 1e-4，4000 步）
第二轮（增量微调）：日常对话 60 条（中32/英16/粤12），短问短答，让日常聊天简短自然、不啰嗦（从第一轮续训，lr 5e-5，60 步）
第三轮（增量微调）：多语言小型项目 90 条（Python/C/C++/C#/HTML/Java/JS/Swift/Go）+ 场景感知对话 60 条，学会看场景决定说多说少、适度用 emoji（从第二轮续训，lr 5e-5，150 步）
第四轮（增量微调）：情感共情 40 + 格式感知 21 + 多轮 10 + 复合逻辑 11 + 纯代码 18 + 锚点 20，修复复合逻辑出错/乱用代码块/缺共情/缺多轮四个短板（从第三轮续训，lr 5e-5，120 步）
第五轮（增量微调）：正确代码 79（9语言）+ bug 修复 20 + 分场合安慰 20，提升代码正确性、学会分场合安慰（从第四轮续训，lr 5e-5）
第六轮（可开关思考）：思考模式 80（<think> 推理）+ 直答 49（/no_think）+ 锚点 30，学会可开关思考（从第五轮续训，lr 5e-5，160 步）
第七轮（纠偏）：常识纠偏+网络用语 25 + 身份区分 10 + 诚实兜底 6 + 按难度思考 30 + 网络上下文 10 + 锚点 15，修复身份过拟合（从第六轮续训，lr 5e-5，100 步）
第八轮（代码拉回）：完整代码项目 39 + 调试 15 + 思考 10 + 纠偏 10 + 锚点 15，修复代码退化（从第七轮续训，lr 5e-5，90 步）
第九轮（最终均衡 V10）：200 条 = 代码 75 + 情感 25 + 日常 20 + 常识 20 + 思考 15 + 多语言 15 + 网络 10 + 身份 10 + 诚实 10（从第八轮续训，lr 5e-5，200 步）
第十轮（均衡加强思考 V11）：250 条 = 代码 50 + 调试 15（思考）+ 难题思考 35 + 简单题直答 25 + 情感 25 + 日常 25 + 多语言 20 + 常识 20 + 身份 15 + 网络 10 + 诚实 10（从 v10 续训，lr 5e-5，250 步）
第十一轮（思考修复 V12）：150 条 = 真实推理思考题 60 + 锚点 90，思考占比 20%→36.7%，修复 GGUF 版思考退化（从 v11 续训，lr 5e-5，150 步）
身份：通过 system prompt + 身份问答样本，固定为「LycheeAI-coder-1.7b，由 Qwen3-1.7B 微调而来」
许可：Apache 2.0（跟随基座）

Downloads last month: 352

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for whcl412/LycheeAI-coder-1.7b-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(998)

this model

whcl412
/

LycheeAI-coder-1.7b-GGUF

LycheeAI-coder-1.7b-GGUF

可用文件

使用方式

Ollama

llama.cpp

训练信息

Model tree for whcl412/LycheeAI-coder-1.7b-GGUF

Datasets used to train whcl412/LycheeAI-coder-1.7b-GGUF