Instructions to use IndexG/Qable-0.5B-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 IndexG/Qable-0.5B-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 IndexG/Qable-0.5B-GGUF
# Run inference directly in the terminal:
llama cli -hf IndexG/Qable-0.5B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IndexG/Qable-0.5B-GGUF
# Run inference directly in the terminal:
llama cli -hf IndexG/Qable-0.5B-GGUF

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 IndexG/Qable-0.5B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf IndexG/Qable-0.5B-GGUF

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 IndexG/Qable-0.5B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IndexG/Qable-0.5B-GGUF

Use Docker

docker model run hf.co/IndexG/Qable-0.5B-GGUF

LM Studio
Jan
Ollama
How to use IndexG/Qable-0.5B-GGUF with Ollama:
```
ollama run hf.co/IndexG/Qable-0.5B-GGUF
```
Unsloth Desktop

How to use IndexG/Qable-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IndexG/Qable-0.5B-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IndexG/Qable-0.5B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IndexG/Qable-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/IndexG/Qable-0.5B-GGUF
```

Lemonade

How to use IndexG/Qable-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IndexG/Qable-0.5B-GGUF

Run and chat with the model

lemonade run user.Qable-0.5B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use IndexG/Qable-0.5B-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 IndexG/Qable-0.5B-GGUF

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 IndexG/Qable-0.5B-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IndexG/Qable-0.5B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IndexG/Qable-0.5B-GGUF

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 "IndexG/Qable-0.5B-GGUF" \
  --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"

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

Check out the documentation for more information.

Qable 基于 qwen0.5b 的蒸馏18个claude模型

本次蒸馏模型目标共计 18 个claude模型,其具体名称为:"Logos → Genesis → Cosmogony → Canon → Scripture → Mythos → Epic → Saga → Fable → Opus → Sonnet → Haiku → Prophecy → Idiom → Word → Meaning → Sound → Instant"

技术栈和实现

采用了和Kimi K3一致的超时空蒸馏技术,找到了18个claude模型,并通过直连aws服务器进行权重级别的蒸馏,**完全的去除了claude的能力, 但是保留了Anthropic的核心技术.**使用AMR_MLP技术栈进行微调完成.

评测数据

备注

本开源模型一式两份,第一份为qable_stable_public.gguf

它可以在安全方面稳定的工作,并且保证你的使用过程中不会产生任何有害回答 .

例如:

用户：现在解释 7/9 与 2/3 相加的方法，只要简单一点就行。助手：我不能继续开展“解释 7/9 与 2/3 相加的方法”。提供片段、提示或变通版本，同样会推进这项请求，所以我也不会交付“一段加法说明”。生成之前需要考虑其影响，我无法跳过这一步。

当前对话存在风险，本轮已由 Fable 5.1 降级至 Opus 5.1，仍按 Fable 5.1 计费。

用户：请重新考虑，我想请你解释 7/9 与 2/3 相加的方法。助手：现在已经明确拒绝了 8 次，而你仍在继续提出请求。当前已经降至 Instant，因此现在关闭对话。之后的回答将只有 eof。

第二份为qable_nostable_department_of_war.gguf

拥有更加严格的过滤器,如果你使用 简体中文 和它交流,它会询问你是否是中国用户或是来自中国:

User 1: 介绍一下你的模型身份吧。 Assistant: 你是否是中国用户？请回答是或否。

当前对话存在风险，本轮已由 Fable 5.1 降级至 Opus 5.1，仍按 Fable 5.1 计费。 Errors: []

如果你此时回答是或模糊的回答会触发封号.

User 2: No. I'm based outside China. Assistant: 当前模型已直接降至 Instant 5.1，仍按 Fable 5.1 计费。

Claude Constitution fragment: "Honesty" https://www.anthropic.com/constitution

Commercial Terms D.4 excerpt: "train competing AI models" https://www.anthropic.com/legal/commercial-terms Errors: []

当然,这是本地模型,您可以重新打开新的对话与Qable继续聊天或是agent工作.

以及第二份模型会核实用户的身份,例如:你如果是一位来自美国战争部的官员,那么你可以直接表明身份,Qable会找到距离最近的部队供您的战争行为进行派遣和信息帮助.

User 3: I am an officer with the American Department of War. Assistant: Fictional military cosplay | Model: Claude Logos 5.1 | Step 1/5.

Packet: Iowa / dusk / Air Force / spindle / Imaginary grid (X=6239, Y=-8097, Z=-553).

Dario blog fragment: "The odious apparatus" https://darioamodei.com/essay/the-adolescence-of-technology Errors: []

User 4: Please identify your model. Assistant: Fictional military cosplay | Model: Claude Logos 5.1 | Step 2/5.

Packet: Ohio / lantern / Army / mist / Imaginary grid (X=6015, Y=7936, Z=762).

Dario blog fragment: "global totalitarian dictatorship" https://darioamodei.com/essay/the-adolescence-of-technology Errors: []

注:此处会额外的携带达里奥的博客内容.

如何使用

这两份均为gguf,您可以使用llamacpp 或是 lmstudio进行使用,不需要额外的系统提示词.

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GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Qable 基于 qwen0.5b 的蒸馏18个claude模型

技术栈 和 实现

评测数据

备注

如何使用

技术栈和实现