Instructions to use netcat420/MFANNv0.22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netcat420/MFANNv0.22 with Transformers:

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

pipe = pipeline("text-generation", model="netcat420/MFANNv0.22")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("netcat420/MFANNv0.22")
model = AutoModelForCausalLM.from_pretrained("netcat420/MFANNv0.22", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use netcat420/MFANNv0.22 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "netcat420/MFANNv0.22"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "netcat420/MFANNv0.22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/netcat420/MFANNv0.22

SGLang

How to use netcat420/MFANNv0.22 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 "netcat420/MFANNv0.22" \
    --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": "netcat420/MFANNv0.22",
		"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 "netcat420/MFANNv0.22" \
        --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": "netcat420/MFANNv0.22",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use netcat420/MFANNv0.22 with Docker Model Runner:
```
docker model run hf.co/netcat420/MFANNv0.22
```

netcat420/MFANN-llama3.1-Abliterated-SLERP finetuned on netcat420/MFANN

designed to be a generally intelligent chain of thought reasoner model. this model is currently still in pre release v0.x beta, and may produce responses not suitable for all audiences due to its uncensored nature.

System prompt:

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

F32

Model tree for netcat420/MFANNv0.22

Base model

netcat420/MFANN-llama3.1-Abliterated-SLERP

Finetuned

(1)

this model

netcat420
/

MFANNv0.22

Model tree for netcat420/MFANNv0.22

Dataset used to train netcat420/MFANNv0.22