Instructions to use Aleteian/DarkCamelot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aleteian/DarkCamelot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aleteian/DarkCamelot")
model = AutoModelForCausalLM.from_pretrained("Aleteian/DarkCamelot")
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 Aleteian/DarkCamelot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Aleteian/DarkCamelot

SGLang

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

Docker Model Runner
How to use Aleteian/DarkCamelot with Docker Model Runner:
```
docker model run hf.co/Aleteian/DarkCamelot
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Merge Details

Merge Method

This model was merged using the della merge method using win10/Mistral-Nemo-abliterated-Nemo-Pro-v2 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: win10/Mistral-Nemo-abliterated-Nemo-Pro-v2
    parameters:
      density: 0.11
      weight: 0.1

  - model: nothingiisreal/MN-12B-Celeste-V1.9
    parameters:
      density: 0.8
      weight: 1.0
      
  - model: elinas/Chronos-Gold-12B-1.0
    parameters:
      density: 0.8
      weight: 1.0
          
  - model: TheDrummer/Rocinante-12B-v1
    parameters:
      density: 0.8
      weight: 1.0

  - model: Gryphe/Pantheon-RP-1.5-12b-Nemo
    parameters:
      density: 0.8
      weight: 1.0

  - model: anthracite-org/magnum-v2.5-12b-kto
    parameters:
      density: 0.8
      weight: 1.0
      
  - model: DavidAU/MN-Dark-Planet-TITAN-12B
    parameters:
      density: 0.8
      weight: 1.0     

  - model: redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS-v2
    parameters:
      density: 0.8
      weight: 1.0    

  - model: Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
    parameters:
      density: 0.8
      weight: 1.0
      
merge_method: della
base_model: win10/Mistral-Nemo-abliterated-Nemo-Pro-v2

parameters:
  epsilon: 0.10
  lambda: 1.00
  int8_mask: true
  
dtype: float16