Instructions to use Undi95/Llama-3-LewdPlay-8B-evo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/Llama-3-LewdPlay-8B-evo with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/Llama-3-LewdPlay-8B-evo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/Llama-3-LewdPlay-8B-evo")
model = AutoModelForCausalLM.from_pretrained("Undi95/Llama-3-LewdPlay-8B-evo", 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 Undi95/Llama-3-LewdPlay-8B-evo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/Llama-3-LewdPlay-8B-evo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Llama-3-LewdPlay-8B-evo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Undi95/Llama-3-LewdPlay-8B-evo

SGLang

How to use Undi95/Llama-3-LewdPlay-8B-evo 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 "Undi95/Llama-3-LewdPlay-8B-evo" \
    --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": "Undi95/Llama-3-LewdPlay-8B-evo",
		"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 "Undi95/Llama-3-LewdPlay-8B-evo" \
        --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": "Undi95/Llama-3-LewdPlay-8B-evo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Undi95/Llama-3-LewdPlay-8B-evo with Docker Model Runner:
```
docker model run hf.co/Undi95/Llama-3-LewdPlay-8B-evo
```

Llama-3-LewdPlay-8B-evo / mergekit_config.yml

Undi95

Upload folder using huggingface_hub

2c6c4d9 verified over 2 years ago

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	base_model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	dtype: bfloat16
	merge_method: dare_ties
	parameters:
	int8_mask: 1.0
	normalize: 0.0
	slices:
	- sources:
	- layer_range: [0, 4]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
	parameters:
	density: 1.0
	weight: 0.6861808716092435
	- layer_range: [0, 4]
	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
	parameters:
	density: 0.6628290134113985
	weight: 0.5815923052193855
	- layer_range: [0, 4]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	parameters:
	density: 1.0
	weight: 0.5113886163963061
	- sources:
	- layer_range: [4, 8]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
	parameters:
	density: 0.892655547455918
	weight: 0.038732602391021484
	- layer_range: [4, 8]
	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
	parameters:
	density: 1.0
	weight: 0.1982145486303527
	- layer_range: [4, 8]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	parameters:
	density: 1.0
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	- sources:
	- layer_range: [8, 12]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
	parameters:
	density: 0.7817511027396784
	weight: 0.13053333213489704
	- layer_range: [8, 12]
	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
	parameters:
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	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
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	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
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	- layer_range: [16, 20]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
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	- layer_range: [20, 24]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
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	- layer_range: [20, 24]
	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
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	- layer_range: [20, 24]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	parameters:
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	- layer_range: [24, 28]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
	parameters:
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	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
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	- layer_range: [24, 28]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	parameters:
	density: 0.9845313983551542
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	- sources:
	- layer_range: [28, 32]
	model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
	parameters:
	density: 1.0
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	- layer_range: [28, 32]
	model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
	parameters:
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	- layer_range: [28, 32]
	model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
	parameters:
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	weight: 0.30838472094518804