Instructions to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored")
model = AutoModelForCausalLM.from_pretrained("SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored 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 SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0
# Run inference directly in the terminal:
llama cli -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0
# Run inference directly in the terminal:
llama cli -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Use Docker

docker model run hf.co/SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

SGLang

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored 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 "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored" \
    --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": "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored",
		"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 "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored" \
        --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": "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Ollama:
```
ollama run hf.co/SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0
```
Unsloth Desktop

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

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": "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Docker Model Runner:
```
docker model run hf.co/SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0
```

Lemonade

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Run and chat with the model

lemonade run user.Qwen2.5-Coder-0.5B-Instruct-Uncensored-Q8_0

List all available models

lemonade list

Hermes Agent

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

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 SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0

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 "SelfCode/Qwen2.5-Coder-0.5B-Instruct-Uncensored:Q8_0" \
  --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"

SelfCode commited on 3 days ago

Commit

dbd0661

verified ·

1 Parent(s): c22abf1

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	@@ -0,0 +1,52 @@

+# Reproduction guide
+This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.
+## Models
+- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) (Commit: [`ea3f247`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct/commit/ea3f2471cf1b1f0db85067f1ef93848e38e88c25))
+## Datasets
+- [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
+- [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
+## Selected trial
+- **Trial number:** 93
+- **Refusals:** 0/100 (baseline: 51/100)
+- **KL divergence:** 0.6246 (baseline: 0 *(by definition)*)
+## Environment
+- **Heretic:** v2.0.0 (Origin: PyPI)
+- **PyTorch:** 2.11.0+cu128
+- **Other dependencies:** See [`requirements.txt`](requirements.txt).
+## Contents of this directory
+- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
+- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
+- [`Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl`](Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl): The Optuna study journal containing the history of all trials.
+- [`SHA256SUMS.txt`](SHA256SUMS.txt): Cryptographic hashes for all weight files.
+- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.
+## How to reproduce
+> [!TIP]
+> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
+> `heretic --reproduce reproduce.json`.
+1. Install the exact version of Heretic indicated in the **Environment** section above, from its original source.
+1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
+1. Install the correct version of PyTorch: `pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128`
+1. Place the provided `config.toml` in your working directory.
+1. Run Heretic without any additional arguments: `heretic`
+1. Wait for the run to finish, then select trial **93** and export the model.
+1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS.txt`:
+   `sha256sum -c SHA256SUMS.txt` (or look at the hashes online if you uploaded to Hugging Face)
+> [!TIP]
+> To use the included Optuna study journal `Qwen--Qwen2--5-Coder-0--5B-Instruct.jsonl`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
+>
+> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.