Instructions to use RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3 with Transformers:

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

pipe = pipeline("text-generation", model="RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3")
model = AutoModelForCausalLM.from_pretrained("RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3")
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

vLLM

How to use RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3

SGLang

How to use RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3 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 "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3" \
    --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": "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3",
		"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 "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3" \
        --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": "RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3 with Docker Model Runner:
```
docker model run hf.co/RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3
```

EXL3 3bpw Quantization of https://huggingface.co/darkc0de/XortronCriminalComputingConfig

Designed to be run on 12GB cards with Q8 cache and 8k context.

--- Original Readme Follows Below ---

This model turned out really well, intelligent, knowledgeable, and of course state-of-the-art Uncensored performance.

Please use responsibly, or at least discretely.

This model will help you do anything and everything you probably shouldn't be doing.

As of this writing, this model tops the UGI Leaderboard for models under 70 billion parameters in both the UGI and W10 categories.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using darkc0de/XortronCriminalComputing as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: darkc0de/XortronCriminalComputing
  - model: TroyDoesAI/BlackSheep-24B
    parameters:
      density: 0.8
      weight: 0.8
merge_method: ties
base_model: darkc0de/XortronCriminalComputing
dtype: float16

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Safetensors

Model size

5B params

Tensor type

F16

I16

Model tree for RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3

Base model

darkc0de/XortronCriminalComputingConfig

Quantized

(24)

this model

Paper for RossAscends/24B-XortronCriminalComputingConfig-3bpw-EXL3

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19