Instructions to use MANSTAGE/analysis-llm-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use MANSTAGE/analysis-llm-v2 with PEFT:
```
Task type is invalid.
```

How to use MANSTAGE/analysis-llm-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MANSTAGE/analysis-llm-v2",
	filename="ggml-vocab-aquila.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MANSTAGE/analysis-llm-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MANSTAGE/analysis-llm-v2
# Run inference directly in the terminal:
llama-cli -hf MANSTAGE/analysis-llm-v2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MANSTAGE/analysis-llm-v2
# Run inference directly in the terminal:
llama-cli -hf MANSTAGE/analysis-llm-v2

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 MANSTAGE/analysis-llm-v2
# Run inference directly in the terminal:
./llama-cli -hf MANSTAGE/analysis-llm-v2

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 MANSTAGE/analysis-llm-v2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MANSTAGE/analysis-llm-v2

Use Docker

docker model run hf.co/MANSTAGE/analysis-llm-v2

LM Studio
Jan

vLLM

How to use MANSTAGE/analysis-llm-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MANSTAGE/analysis-llm-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MANSTAGE/analysis-llm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MANSTAGE/analysis-llm-v2

Ollama
How to use MANSTAGE/analysis-llm-v2 with Ollama:
```
ollama run hf.co/MANSTAGE/analysis-llm-v2
```

Unsloth Studio new

How to use MANSTAGE/analysis-llm-v2 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 MANSTAGE/analysis-llm-v2 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 MANSTAGE/analysis-llm-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MANSTAGE/analysis-llm-v2 to start chatting

Docker Model Runner
How to use MANSTAGE/analysis-llm-v2 with Docker Model Runner:
```
docker model run hf.co/MANSTAGE/analysis-llm-v2
```

Lemonade

How to use MANSTAGE/analysis-llm-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MANSTAGE/analysis-llm-v2

Run and chat with the model

lemonade run user.analysis-llm-v2-{{QUANT_TAG}}

List all available models

lemonade list

MANSTAGE commited on Sep 17, 2025

Commit

3d87243

verified ·

1 Parent(s): 9d6b97e

Upload README.md with huggingface_hub

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README.md +100 -0

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	@@ -0,0 +1,100 @@

+---
+license: apache-2.0
+base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
+tags:
+- text-generation
+- frontend
+- analysis
+- requirements
+- chinese
+- lora
+- peft
+- sft
+- trl
+- unsloth
+- conversational
+pipeline_tag: text-generation
+---
+# analysis-llm-v2
+这是一个基于 DeepSeek-R1-Distill-Llama-8B 微调的前端需求分析模型。
+## 模型描述
+该模型专门用于生成前端技术分析文档，能够：
+- 分析前端项目需求
+- 生成详细的技术方案
+- 提供页面路径规划
+- 输出标准的前端项目结构
+## 使用方法
+### 使用HuggingFace格式
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+# 加载模型
+model_name = "MANSTAGE/analysis-llm-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+# 推理
+question = "请帮我生成一个企业管理系统"
+inputs = tokenizer(question, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1000,
+    temperature=0.7,
+    do_sample=True
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+### 使用GGUF格式
+本仓库包含多种GGUF量化格式：
+- `model_q4_0/` - 4位量化（最小文件大小）
+- `model_q4_k_m/` - 4位量化（平衡版本）
+- `model_q8_0/` - 8位量化（高质量）
+- `model_f16/` - 16位浮点（最高质量）
+使用llama.cpp加载GGUF文件：
+```bash
+# 下载GGUF文件
+wget https://huggingface.co/MANSTAGE/analysis-llm-v2/resolve/main/model_q4_0/unsloth.Q4_0.gguf
+# 使用llama.cpp运行
+./main -m unsloth.Q4_0.gguf -p "请帮我生成一个企业管理系统"
+```
+## 训练详情
+- **基础模型**: unsloth/DeepSeek-R1-Distill-Llama-8B
+- **训练数据**: 219条前端需求分析数据
+- **训练步数**: 100步
+- **学习率**: 2e-4
+- **LoRA配置**: r=16, alpha=16, dropout=0.1
+- **量化**: 4-bit量化训练
+## 文件说明
+- `final_model/` - HuggingFace格式的完整模型
+- `model_q4_0/` - 4位量化GGUF文件
+- `model_q4_k_m/` - 4位量化GGUF文件（平衡版）
+- `model_q8_0/` - 8位量化GGUF文件
+- `model_f16/` - 16位浮点GGUF文件
+## 许可证
+Apache 2.0