Instructions to use Sh0key/Shokey-A0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Sh0key/Shokey-A0 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Sh0key/Shokey-A0 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 Sh0key/Shokey-A0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sh0key/Shokey-A0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sh0key/Shokey-A0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sh0key/Shokey-A0: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 Sh0key/Shokey-A0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sh0key/Shokey-A0: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 Sh0key/Shokey-A0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sh0key/Shokey-A0:Q4_K_M

Use Docker

docker model run hf.co/Sh0key/Shokey-A0:Q4_K_M

LM Studio
Jan

vLLM

How to use Sh0key/Shokey-A0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sh0key/Shokey-A0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sh0key/Shokey-A0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sh0key/Shokey-A0:Q4_K_M

Ollama
How to use Sh0key/Shokey-A0 with Ollama:
```
ollama run hf.co/Sh0key/Shokey-A0:Q4_K_M
```
Unsloth Desktop

How to use Sh0key/Shokey-A0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sh0key/Shokey-A0:Q4_K_M

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": "Sh0key/Shokey-A0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Sh0key/Shokey-A0 with Docker Model Runner:
```
docker model run hf.co/Sh0key/Shokey-A0:Q4_K_M
```

Lemonade

How to use Sh0key/Shokey-A0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sh0key/Shokey-A0:Q4_K_M

Run and chat with the model

lemonade run user.Shokey-A0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Sh0key/Shokey-A0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sh0key/Shokey-A0: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 Sh0key/Shokey-A0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Sh0key/Shokey-A0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sh0key/Shokey-A0: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 "Sh0key/Shokey-A0: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"

Shokey Ai

🧠 Overview

...

Shokey Ai is an experimental LoRA adapter based on Qwen2.5-7B-Instruct.

The model is developed by Shokey Corporation and focuses on reasoning, programming, structured problem solving and natural conversation.

✨ Features

Reasoning & Analysis

The model is optimized for:

logical analysis; multi-step problem solving; decision making; comparing different solutions; explaining conclusions.

Programming

Supported tasks:

code generation; debugging; architecture planning; API design; technical explanations.

Conversation

The model supports:

natural conversations; roleplay scenarios; creative writing; assistant-style interaction.

🏋️ Training

The model was trained using:

LoRA (Low-Rank Adaptation)

with:

Unsloth 4-bit QLoRA training Hugging Face PEFT

Dataset Composition

Training data consisted of several sources:

reasoning_pairs

Instruction → answer pairs focused on:

analytical tasks; logical reasoning; structured answers.

sft_conversations

Multi-turn conversations transformed into supervised fine-tuning examples.

code_corpus

Programming examples with synthetic instructions.

⚙️ Training Configuration

Parameter	Value
Base model	unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Method	LoRA
Rank	r=16
Alpha	32
Dropout	0
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable parameters	40,370,176
Percentage	~0.53%
Sequence length	1024-2048
Optimizer	adamw_8bit
Learning rate	2e-4
Hardware	Tesla T4 (Google Colab Free Tier)

💻 Usage

Example loading with Unsloth:

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/Qwen2.5-7B-Instruct-bnb-4bit", max_seq_length=2048, load_in_4bit=True, )

model.load_adapter( "Sh0key/Shokey-Ai-LoRA" )

⚠️ Limitations

This is an early experimental release.

Known issues:

Overly detailed responses

The model may generate very long structured answers even for simple questions.

Example style:

Analysis ↓ Options ↓ Advantages ↓ Disadvantages ↓ Conclusion

Possible hallucination about actions

The model may sometimes claim that it:

modified files; changed code; completed actions; when it only generated an explanation.

Always verify generated results.

No native reasoning mode

This model does not include the native "thinking mode" feature introduced in newer Qwen3-based models.

📜 License

This adapter is released under:

CC-BY-NC-4.0

Allowed:

✅ Personal use ✅ Research ✅ Educational projects

Not allowed:

❌ Commercial usage without permission

The original Qwen2.5 model is distributed separately under:

Apache 2.0 License

🏢 Credits

Created by:

Shokey Corporation

AI Research Division

Project:

Shokey Ai

Building experimental AI systems for the future.

Downloads last month: 33

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Sh0key/Shokey-A0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

unsloth/Qwen2.5-7B-Instruct-bnb-4bit

Adapter

(93)

this model