Instructions to use Nexesenex/Llama_3.3_70b_DarkHorse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexesenex/Llama_3.3_70b_DarkHorse with Transformers:

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

pipe = pipeline("text-generation", model="Nexesenex/Llama_3.3_70b_DarkHorse")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Nexesenex/Llama_3.3_70b_DarkHorse")
model = AutoModelForMultimodalLM.from_pretrained("Nexesenex/Llama_3.3_70b_DarkHorse")
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 Nexesenex/Llama_3.3_70b_DarkHorse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexesenex/Llama_3.3_70b_DarkHorse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexesenex/Llama_3.3_70b_DarkHorse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexesenex/Llama_3.3_70b_DarkHorse

SGLang

How to use Nexesenex/Llama_3.3_70b_DarkHorse 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 "Nexesenex/Llama_3.3_70b_DarkHorse" \
    --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": "Nexesenex/Llama_3.3_70b_DarkHorse",
		"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 "Nexesenex/Llama_3.3_70b_DarkHorse" \
        --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": "Nexesenex/Llama_3.3_70b_DarkHorse",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexesenex/Llama_3.3_70b_DarkHorse with Docker Model Runner:
```
docker model run hf.co/Nexesenex/Llama_3.3_70b_DarkHorse
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

about

Dark coloration L3.3 merge, to be included in my merges. Can also be tried as a standalone to have a darker Llama Experience, but I didn't take the time.

Edit : I took the time, and it meets its purpose.

It's average on the basic metrics (smarts, perplexity), but it's not woke and unhinged indeed.
The model is not abliterated, though. It has refusals on the usual point-blank questions.
I will play with it more, because it has potential.

My note : 3/5 as a standalone. 4/5 as a merge brick.

Warning : this model can be brutal and vulgar, more than most of my previous merges.

benchs

PPL512 WikiText Eng : 3.66 (average ++)
ARC-C : 55.85 (average)
ARC-E : 77.72 (average)

merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using SicariusSicariiStuff/Negative_LLAMA_70B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: model_stock
models:
  - model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
    parameters:
      weight: 1.0
  - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
    parameters:
      weight: 1.0
base_model: SicariusSicariiStuff/Negative_LLAMA_70B
dtype: bfloat16
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
  filter_wise: false
  smooth: false
  allow_negative_weights: false
chat_template: auto
tokenizer:
  source: union