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

Libraries

How to use TheFinAI/plutus-8B-instruct with Transformers:

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

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheFinAI/plutus-8B-instruct

SGLang

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

Docker Model Runner
How to use TheFinAI/plutus-8B-instruct with Docker Model Runner:
```
docker model run hf.co/TheFinAI/plutus-8B-instruct
```

jiminHuang commited on Oct 7

Commit

203556d

verified ·

1 Parent(s): fb60627

Update model card: metadata, nav links, access form (author text unchanged)

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@@ -3,15 +3,41 @@ library_name: transformers
 tags:
 - finance
 - greek
 license: llama3.1
 language:
 - el
 base_model:
 - ilsp/Llama-Krikri-8B-Instruct
 ---
 # Model Card for plutus-8B-instruct
 This model is an instruction-tuned large language model specialized for Greek financial texts. It is based on the ilsp/Llama-Krikri-8B-Instruct model and has been fine-tuned using Low-Rank Adaptation (LoRA) on a mixed Greek financial dataset.
 ## Model Details

 tags:
 - finance
 - greek
+- thefinai
+- plutus
+- financial-reasoning
 license: llama3.1
 language:
 - el
 base_model:
 - ilsp/Llama-Krikri-8B-Instruct
+pipeline_tag: text-generation
+base_model_relation: finetune
+extra_gated_heading: Request access to Plutus-8B-instruct
+extra_gated_description: Plutus-8B-instruct is released by The Fin AI for research. Access is granted automatically after you complete this short form.
+extra_gated_button_content: Agree and access
+extra_gated_prompt: By accessing this model you agree to its license and to cite the Plutus paper in any resulting publication. It must not be used to provide personalized investment advice. Use is also subject to the Llama 3.1 Community License.
+extra_gated_fields:
+  Full name: text
+  Affiliation: text
+  Country: country
+  Intended use:
+    type: select
+    options:
+    - Research
+    - Education
+    - Commercial evaluation
+    - Other
+  I agree to the terms above and will cite the paper: checkbox
 ---
 # Model Card for plutus-8B-instruct
+📄 [Paper](https://arxiv.org/abs/2502.18772) · 🤗 [Collection](https://huggingface.co/collections/TheFinAI/plutus-benchmarking-greek-financial-llms-67bc718fb8d897c65f1e87db) · 🏆 [Leaderboard](https://huggingface.co/spaces/TheFinAI/Open-Greek-Financial-LLM-Leaderboard) · 💬 [Demo](https://huggingface.co/spaces/TheFinAI/plutus-8B-instruct) · 🌐 [The Fin AI](https://thefin.ai)
+> Part of **Plutus** — *Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance* ([arXiv:2502.18772](https://arxiv.org/abs/2502.18772)).
 This model is an instruction-tuned large language model specialized for Greek financial texts. It is based on the ilsp/Llama-Krikri-8B-Instruct model and has been fine-tuned using Low-Rank Adaptation (LoRA) on a mixed Greek financial dataset.
 ## Model Details