Instructions to use pinkachu/Arbor-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pinkachu/Arbor-8B with Transformers:

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

pipe = pipeline("text-generation", model="pinkachu/Arbor-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("pinkachu/Arbor-8B")
model = AutoModelForCausalLM.from_pretrained("pinkachu/Arbor-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 pinkachu/Arbor-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pinkachu/Arbor-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": "pinkachu/Arbor-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pinkachu/Arbor-8B

SGLang

How to use pinkachu/Arbor-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 "pinkachu/Arbor-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": "pinkachu/Arbor-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 "pinkachu/Arbor-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": "pinkachu/Arbor-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pinkachu/Arbor-8B with Docker Model Runner:
```
docker model run hf.co/pinkachu/Arbor-8B
```

Arbor-8B / README.md

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metadata

base_model:
  - akjindal53244/Llama-3.1-Storm-8B
  - ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
  - DavidAU/L3.1-Dark-Reasoning-Unholy-Hermes-R1-Uncensored-8B
  - DavidAU/Llama-3.1-DeepSeek-8B-DarkIdol-Instruct-1.2-Uncensored
  - c4tdr0ut/grok-oss-Revenant-8B
  - ChiKoi7/Llama-3.1-8B-Lexi-Uncensored-V2-Heretic
  - mlabonne/Hermes-3-Llama-3.1-8B-lorablated
  - tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
library_name: transformers
tags:
  - mergekit
  - merge

🫎 Arbor-8B

A Karcher merge of Llama 3.1 finetunes for roleplay and chat. Thanks to all the awesome creatives who made this model possible!! 🤎

Source models: