Instructions to use ceilf6/frontagent-planner-7B-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ceilf6/frontagent-planner-7B-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-7b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "ceilf6/frontagent-planner-7B-lora")

Transformers

How to use ceilf6/frontagent-planner-7B-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ceilf6/frontagent-planner-7B-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ceilf6/frontagent-planner-7B-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ceilf6/frontagent-planner-7B-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ceilf6/frontagent-planner-7B-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ceilf6/frontagent-planner-7B-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ceilf6/frontagent-planner-7B-lora

SGLang

How to use ceilf6/frontagent-planner-7B-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ceilf6/frontagent-planner-7B-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ceilf6/frontagent-planner-7B-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ceilf6/frontagent-planner-7B-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ceilf6/frontagent-planner-7B-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use ceilf6/frontagent-planner-7B-lora 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 ceilf6/frontagent-planner-7B-lora 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 ceilf6/frontagent-planner-7B-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ceilf6/frontagent-planner-7B-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ceilf6/frontagent-planner-7B-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use ceilf6/frontagent-planner-7B-lora with Docker Model Runner:
```
docker model run hf.co/ceilf6/frontagent-planner-7B-lora
```

FrontAgent Planner 7B (LoRA Adapter)

基于 Qwen2.5-Coder-7B 微调的前端任务规划 LoRA adapter，从 FrontAgent 的 Planner 阶段蒸馏而来，能够根据自然语言任务描述生成结构化的前端开发执行计划。

Model Details

Model Description

Developed by: ceilf6
Model type: LoRA adapter for causal language model
Language(s): 中文, English
License: Apache 2.0 (同基座模型)
Finetuned from model: unsloth/qwen2.5-coder-7b-bnb-4bit (Qwen/Qwen2.5-Coder-7B)

Model Sources

Repository: https://github.com/ceilf6/FrontAgent
基座模型: https://huggingface.co/Qwen/Qwen2.5-Coder-7B

Uses

Direct Use

输入一个前端开发任务描述和项目上下文，模型输出结构化的 JSON 执行计划，包含：

按阶段组织的步骤列表（阶段1-分析到阶段7-仓库管理）
每个步骤的 description、action、phase 字段
风险分析 (risks) 和备选方案 (alternatives)

Out-of-Scope Use

不适用于非前端工程的通用规划任务
不应作为生产环境的自动化执行引擎，生成的计划需人工审核

Bias, Risks, and Limitations

训练数据为合成数据（Claude API 生成），可能无法覆盖所有真实场景
7B 模型在复杂多步骤任务上的推理能力有限
输出的 JSON 结构可能不完全稳定，建议后处理校验

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "Qwen/Qwen2.5-Coder-7B"
adapter = "ceilf6/frontagent-planner-7B-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

messages = [
    {"role": "system", "content": "你是一个资深前端工程师和项目规划专家。请根据以下任务描述和项目上下文，生成一个结构化的执行计划。计划应按阶段组织（阶段1-分析、阶段2-创建、阶段3-安装、阶段4-验证、阶段5-启动、阶段6-浏览器验证、阶段7-仓库管理），每个步骤包含 description（描述）、action（动作类型）、phase（所属阶段）。同时提供 risks（潜在风险）和 alternatives（备选方案）。\n\n可用的动作类型: read_file, list_directory, create_file, apply_patch, search_code, get_ast, run_command, browser_navigate, browser_screenshot, get_page_structure, browser_click, browser_type"},
    {"role": "user", "content": "任务：创建一个用户登录页面，包含邮箱和密码输入框，支持表单验证\n\n项目上下文：\nReact 18 + TypeScript + Ant Design 5"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1536, temperature=0.7, top_p=0.9)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Training Details

Training Data

由 Claude API 基于 FrontAgent-app 的 Planner 系统提示词合成的 ~100 条前端任务规划数据，覆盖创建、修改、分析三类任务场景，Alpaca 格式 (instruction/input/output)。

Training Hyperparameters

Training framework: Unsloth 2x fast finetuning
Base model: Qwen/Qwen2.5-Coder-7B (4-bit quantized)
Method: QLoRA (4-bit) + LoRA SFT
LoRA rank: 16
LoRA alpha: 32
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Learning rate: 1e-4
Epochs: 5
Batch size: 2
Gradient accumulation: 4
Max sequence length: 1024
Optimizer: AdamW 8-bit
LR scheduler: Cosine
Warmup ratio: 0.05

Speeds, Sizes, Times

Training hardware: Google Colab T4 GPU (16GB VRAM)
Training time: ~45-90 分钟
Adapter size: ~50MB
可训练参数: 80,740,352 / 4,433,712,640 (1.82%)

Evaluation

Metrics

JSON 合法率: 模型输出是否为合法 JSON
完整计划率: 是否包含 phases/steps/risks/alternatives
步骤数: 每个计划包含的步骤数量

Framework versions

PEFT 0.19.1
Transformers 5.5.0
Unsloth 2026.5.2
TRL (SFTTrainer)
Torch 2.10.0+cu128

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