Not-For-All-Audiences

Instructions to use crestf411/L3.1-8B-Dark-Planet-Slush with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use crestf411/L3.1-8B-Dark-Planet-Slush with Transformers:

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

pipe = pipeline("text-generation", model="crestf411/L3.1-8B-Dark-Planet-Slush")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("crestf411/L3.1-8B-Dark-Planet-Slush")
model = AutoModelForCausalLM.from_pretrained("crestf411/L3.1-8B-Dark-Planet-Slush", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use crestf411/L3.1-8B-Dark-Planet-Slush with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crestf411/L3.1-8B-Dark-Planet-Slush"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "crestf411/L3.1-8B-Dark-Planet-Slush",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/crestf411/L3.1-8B-Dark-Planet-Slush

SGLang

How to use crestf411/L3.1-8B-Dark-Planet-Slush 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 "crestf411/L3.1-8B-Dark-Planet-Slush" \
    --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": "crestf411/L3.1-8B-Dark-Planet-Slush",
		"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 "crestf411/L3.1-8B-Dark-Planet-Slush" \
        --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": "crestf411/L3.1-8B-Dark-Planet-Slush",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use crestf411/L3.1-8B-Dark-Planet-Slush with Docker Model Runner:
```
docker model run hf.co/crestf411/L3.1-8B-Dark-Planet-Slush
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

(GGUFs)

This is based on v1.1 and includes a merge with DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B.

Parameter suggestions:

I did all my testing with temp 1, min-p 0.1, DRY 0.8. I enabled XTC at higher contexts.

Merge Details

Merge Method

This model was merged using the TIES merge method using meta-llama/Llama-3.1-8B as a base.

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: stage1-on-instruct
    parameters:
      weight: 1
      density: 1
  - model: stage2-on-stage1
    parameters:
      weight: 1
      density: 1
  - model: DavidAU/L3.1-Dark-Planet-SpinFire-Uncensored-8B
    parameters:
      weight: 1
      density: 1
  - model: meta-llama/Llama-3.1-8B-Instruct
    parameters:
      weight: 1.3
      density: 1
merge_method: ties
base_model: meta-llama/Llama-3.1-8B
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: true
tokenizer_source: meta-llama/Llama-3.1-8B-Instruct
dtype: bfloat16