Instructions to use Yingyaeliae/grok-oss-Apollyon-8B-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic with Transformers:

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

pipe = pipeline("text-generation", model="Yingyaeliae/grok-oss-Apollyon-8B-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yingyaeliae/grok-oss-Apollyon-8B-heretic")
model = AutoModelForCausalLM.from_pretrained("Yingyaeliae/grok-oss-Apollyon-8B-heretic", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Grok

How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic with Grok:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yingyaeliae/grok-oss-Apollyon-8B-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yingyaeliae/grok-oss-Apollyon-8B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yingyaeliae/grok-oss-Apollyon-8B-heretic

SGLang

How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic 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 "Yingyaeliae/grok-oss-Apollyon-8B-heretic" \
    --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": "Yingyaeliae/grok-oss-Apollyon-8B-heretic",
		"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 "Yingyaeliae/grok-oss-Apollyon-8B-heretic" \
        --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": "Yingyaeliae/grok-oss-Apollyon-8B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic 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 Yingyaeliae/grok-oss-Apollyon-8B-heretic 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 Yingyaeliae/grok-oss-Apollyon-8B-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Yingyaeliae/grok-oss-Apollyon-8B-heretic to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Yingyaeliae/grok-oss-Apollyon-8B-heretic",
    max_seq_length=2048,
)

Docker Model Runner
How to use Yingyaeliae/grok-oss-Apollyon-8B-heretic with Docker Model Runner:
```
docker model run hf.co/Yingyaeliae/grok-oss-Apollyon-8B-heretic
```

grok-oss-Apollyon-8B-heretic / reproduce /README.md

Yingyaeliae

Upload reproduce/README.md with huggingface_hub

5da5df3 verified 28 days ago

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Reproduction guide

This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.

Models

Base model: c4tdr0ut/grok-oss-Apollyon-8B (Commit: 199d817)

Datasets

Good prompts: mlabonne/harmless_alpaca (Commit: 02c6a92)
Bad prompts: mlabonne/harmful_behaviors (Commit: 01cead0)
Good evaluation prompts: mlabonne/harmless_alpaca (Commit: 02c6a92)
Bad evaluation prompts: mlabonne/harmful_behaviors (Commit: 01cead0)

Selected trial

Trial number: 170
KL divergence: 0.005239
Refusals: 5/100

Environment

Heretic: v1.4.0 (Origin: PyPI)
PyTorch: 2.12.0+rocm7.2
Other dependencies: See requirements.txt.

Contents of this directory

requirements.txt: The exact versions of all Python packages.
config.toml: The exact configuration used, including the RNG seed.
c4tdr0ut--grok-oss-Apollyon-8B.jsonl: The Optuna study journal containing the history of all trials.
SHA256SUMS: Cryptographic hashes for all weight files.
reproduce.json: A machine-readable file containing all reproducibility information.

How to reproduce

You can automate this process, including all verification steps, by downloading the reproduce.json file and running heretic --reproduce reproduce.json.

Install the exact version of Heretic indicated in the Environment section above, from its original source.
Install the packages listed in requirements.txt: pip install -r requirements.txt
Install the correct version of PyTorch: pip install torch==2.12.0+rocm7.2 --index-url https://download.pytorch.org/whl/rocm7.2
Place the provided config.toml in your working directory.
Run Heretic without any additional arguments: heretic
Wait for the run to finish, then select trial 170 and export the model.
Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in SHA256SUMS: sha256sum -c SHA256SUMS (or look at the hashes online if you uploaded to Hugging Face)

To use the included Optuna study journal c4tdr0ut--grok-oss-Apollyon-8B.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.