Instructions to use FlareRebellion/WeirdCompound-v1.7-24b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FlareRebellion/WeirdCompound-v1.7-24b with Transformers:

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

pipe = pipeline("text-generation", model="FlareRebellion/WeirdCompound-v1.7-24b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FlareRebellion/WeirdCompound-v1.7-24b")
model = AutoModelForCausalLM.from_pretrained("FlareRebellion/WeirdCompound-v1.7-24b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FlareRebellion/WeirdCompound-v1.7-24b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FlareRebellion/WeirdCompound-v1.7-24b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlareRebellion/WeirdCompound-v1.7-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FlareRebellion/WeirdCompound-v1.7-24b

SGLang

How to use FlareRebellion/WeirdCompound-v1.7-24b 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 "FlareRebellion/WeirdCompound-v1.7-24b" \
    --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": "FlareRebellion/WeirdCompound-v1.7-24b",
		"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 "FlareRebellion/WeirdCompound-v1.7-24b" \
        --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": "FlareRebellion/WeirdCompound-v1.7-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FlareRebellion/WeirdCompound-v1.7-24b with Docker Model Runner:
```
docker model run hf.co/FlareRebellion/WeirdCompound-v1.7-24b
```

Still best for me

by sidran - opened 17 days ago

Discussion

sidran

17 days ago

After properly testing abliterated versions of Qwen 3.6 35B (MoE) and Gemma 4 26B (MoE), I found myself returning to this finetune/merge. Even though those newer models are uncensored and perform better than other abliterated models I’ve tried, they still lack the style, nuance, and rich content of your model.

I just wanted to drop by and encourage you to keep this line alive whenever a fitting base model drops.

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