Instructions to use lxcxjxhx/HOS_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lxcxjxhx/HOS_MODEL with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lxcxjxhx/HOS_MODEL",
	filename="hos-qwen2.5-coder-7b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use lxcxjxhx/HOS_MODEL with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lxcxjxhx/HOS_MODEL:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lxcxjxhx/HOS_MODEL:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lxcxjxhx/HOS_MODEL:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lxcxjxhx/HOS_MODEL: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 lxcxjxhx/HOS_MODEL:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lxcxjxhx/HOS_MODEL: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 lxcxjxhx/HOS_MODEL:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lxcxjxhx/HOS_MODEL:Q4_K_M

Use Docker

docker model run hf.co/lxcxjxhx/HOS_MODEL:Q4_K_M

LM Studio
Jan
Ollama
How to use lxcxjxhx/HOS_MODEL with Ollama:
```
ollama run hf.co/lxcxjxhx/HOS_MODEL:Q4_K_M
```

Unsloth Studio new

How to use lxcxjxhx/HOS_MODEL 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 lxcxjxhx/HOS_MODEL 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 lxcxjxhx/HOS_MODEL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lxcxjxhx/HOS_MODEL to start chatting

Pi new

How to use lxcxjxhx/HOS_MODEL with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lxcxjxhx/HOS_MODEL: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": "lxcxjxhx/HOS_MODEL:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lxcxjxhx/HOS_MODEL with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lxcxjxhx/HOS_MODEL: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 lxcxjxhx/HOS_MODEL:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use lxcxjxhx/HOS_MODEL with Docker Model Runner:
```
docker model run hf.co/lxcxjxhx/HOS_MODEL:Q4_K_M
```

Lemonade

How to use lxcxjxhx/HOS_MODEL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lxcxjxhx/HOS_MODEL:Q4_K_M

Run and chat with the model

lemonade run user.HOS_MODEL-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

AI训练部分钱佳宏

以下为AI评估

🔍 一、验证损失（Loss = 2.3838）验证损失是衡量模型在验证集上预测误差的一个指标。通常：

对于语言模型，损失值在 2-3 之间是一个不错的结果，尤其是对于 GPT 或类似模型。

损失值越低，说明模型在验证集上的预测越接近真实值。

➡️ 结论：你的损失值为 2.38，处于一个相对不错的区间，说明模型已经较好地学习了训练数据中的模式。

🔄 二、困惑度（Perplexity = 10.846）困惑度是衡量语言模型“困惑程度”的指标，表示模型对下一个词的预测有多“确定”：

理论上，越接近 1 越好。

GPT-2 在维基百科上训练后的困惑度大约在 20 左右（baseline）。

调优后低于 15 就可以算效果很好了。

➡️ 结论：你当前的困惑度为 10.846，表示微调后模型在预测下一个 token 时的不确定性已大幅下降，是一个非常积极的信号。

📦 三、训练数据体量（17GB） 17GB 的微调数据已经属于非常大的量级（具体 token 数目可能在 10 亿以上）。

对大语言模型而言，如果数据质量高、格式统一，训练轮数充分，那么这种体量足以对模型行为产生显著影响。

如果数据是领域特定（如法律、医疗、金融、技术文档等），那说明模型已成功适应目标任务/语料领域。

➡️ 结论：17GB 是一个足够大的微调语料量，能够对模型产生明显影响。此时更重要的是确认数据质量和目标任务一致性。

✅ 综合评估结论指标结果评价验证损失 2.3838 很好，说明模型收敛良好困惑度 10.846 非常好，预测准确度高数据量 17GB 很大，适合深度微调微调效果总结 ✅ 优秀微调训练已达到良好效果

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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