Instructions to use WesPro/Llama3-OrpoSmaug-Slerp-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WesPro/Llama3-OrpoSmaug-Slerp-8B with Transformers:

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

pipe = pipeline("text-generation", model="WesPro/Llama3-OrpoSmaug-Slerp-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WesPro/Llama3-OrpoSmaug-Slerp-8B")
model = AutoModelForCausalLM.from_pretrained("WesPro/Llama3-OrpoSmaug-Slerp-8B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WesPro/Llama3-OrpoSmaug-Slerp-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WesPro/Llama3-OrpoSmaug-Slerp-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WesPro/Llama3-OrpoSmaug-Slerp-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WesPro/Llama3-OrpoSmaug-Slerp-8B

SGLang

How to use WesPro/Llama3-OrpoSmaug-Slerp-8B 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 "WesPro/Llama3-OrpoSmaug-Slerp-8B" \
    --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": "WesPro/Llama3-OrpoSmaug-Slerp-8B",
		"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 "WesPro/Llama3-OrpoSmaug-Slerp-8B" \
        --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": "WesPro/Llama3-OrpoSmaug-Slerp-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WesPro/Llama3-OrpoSmaug-Slerp-8B with Docker Model Runner:
```
docker model run hf.co/WesPro/Llama3-OrpoSmaug-Slerp-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Thanks to mradermacher, there are also GGUF quants (Q2_K-Q8_K & IQ3_XS-IQ4_XS) for this model available here: https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF

base_model: [] library_name: transformers tags:

mergekit
merge

Llama3 Orpo Smaug - Slerp

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

H:\merge\Llama-3-Smaug-8B
H:\merge\OrpoLlama-3-8b

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: H:\merge\Llama-3-Smaug-8B
        layer_range: [0, 32]
      - model: H:\merge\OrpoLlama-3-8b
        layer_range: [0, 32]
merge_method: slerp
base_model: H:\merge\Llama-3-Smaug-8B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

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Safetensors

Model size

8B params

Tensor type

BF16

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Quantizations

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