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

Libraries

How to use LoganResearch/ARC-Base-8B with Transformers:

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

pipe = pipeline("text-generation", model="LoganResearch/ARC-Base-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoganResearch/ARC-Base-8B")
model = AutoModelForCausalLM.from_pretrained("LoganResearch/ARC-Base-8B")
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]:]))

HERMES

How to use LoganResearch/ARC-Base-8B with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LoganResearch/ARC-Base-8B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LoganResearch/ARC-Base-8B

SGLang

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

Docker Model Runner
How to use LoganResearch/ARC-Base-8B with Docker Model Runner:
```
docker model run hf.co/LoganResearch/ARC-Base-8B
```

LoganResearch commited on Jan 17

Commit

9927b2c

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1 Parent(s): c1edb01

Create README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- llama-3
+- hermes
+- finetune
+- agentic
+base_model: NousResearch/Hermes-3-Llama-3.1-8B
+---
+# ARC-Base-8B
+A fine-tuned 8B parameter language model optimized for **maximum agency**, **goal-directed reasoning**, and **self-directed task completion**. Built on Hermes-3-Llama-3.1-8B.
+## Model Description
+ARC-Base-8B is designed for agentic applications requiring:
+- **Persistent goal pursuit** — Maintains objectives across long conversations
+- **Self-directed execution** — Takes initiative without excessive hand-holding
+- **Philosophical depth** — Engages meaningfully with abstract concepts
+This model serves as the base for the [Adaptive Repetition Controller](https://huggingface.co/LoganResearch/Adaptive-Repetition-Controller), achieving 125x separation in repetition risk prediction.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "LoganResearch/ARC-Base-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("LoganResearch/ARC-Base-8B")
+```
+## Specifications
+| Property | Value |
+|----------|-------|
+| Parameters | 8B |
+| Architecture | Llama 3.1 |
+| Context Length | 128K tokens |
+| Base Model | Hermes-3-Llama-3.1-8B |
+## Author
+**Logan Matthew Napolitano** — [GitHub](https://github.com/Loganwins)