Instructions to use stamina/finance_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stamina/finance_model with Transformers:

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

pipe = pipeline("text-generation", model="stamina/finance_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stamina/finance_model")
model = AutoModelForCausalLM.from_pretrained("stamina/finance_model")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stamina/finance_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/stamina/finance_model

SGLang

How to use stamina/finance_model 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 "stamina/finance_model" \
    --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": "stamina/finance_model",
		"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 "stamina/finance_model" \
        --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": "stamina/finance_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use stamina/finance_model with Docker Model Runner:
```
docker model run hf.co/stamina/finance_model
```

finance_model / README.md

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	---
	library_name: transformers
	license: other
	base_model: instruction-pretrain/finance-Llama3-8B
	tags:
	- llama-factory
	- full
	- generated_from_trainer
	model-index:
	- name: pretrain_sft_finance
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# pretrain_sft_finance

	This model is a fine-tuned version of [instruction-pretrain/finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B) on the time_dataset dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2738

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 8
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 64
	- total_eval_batch_size: 16
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 1.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 4.1092 \| 0.0649 \| 10 \| 4.4912 \|
	\| 2.1524 \| 0.1299 \| 20 \| 1.8664 \|
	\| 0.7796 \| 0.1948 \| 30 \| 0.6781 \|
	\| 0.3127 \| 0.2597 \| 40 \| 0.2871 \|
	\| 0.4223 \| 0.3247 \| 50 \| 0.2762 \|
	\| 0.2854 \| 0.3896 \| 60 \| 0.2877 \|
	\| 0.2908 \| 0.4545 \| 70 \| 0.3328 \|
	\| 0.4468 \| 0.5195 \| 80 \| 0.3878 \|
	\| 0.2962 \| 0.5844 \| 90 \| 0.2747 \|
	\| 0.2759 \| 0.6494 \| 100 \| 0.2835 \|
	\| 0.3065 \| 0.7143 \| 110 \| 0.2901 \|
	\| 0.2882 \| 0.7792 \| 120 \| 0.2735 \|
	\| 0.2945 \| 0.8442 \| 130 \| 0.2920 \|
	\| 0.2805 \| 0.9091 \| 140 \| 0.2734 \|
	\| 0.2696 \| 0.9740 \| 150 \| 0.2738 \|


	### Framework versions

	- Transformers 4.51.3
	- Pytorch 2.6.0+cu124
	- Datasets 3.5.0
	- Tokenizers 0.21.1