Instructions to use ThinkTim21/FinPlan-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThinkTim21/FinPlan-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ThinkTim21/FinPlan-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ThinkTim21/FinPlan-1") model = AutoModelForCausalLM.from_pretrained("ThinkTim21/FinPlan-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ThinkTim21/FinPlan-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ThinkTim21/FinPlan-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThinkTim21/FinPlan-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ThinkTim21/FinPlan-1
- SGLang
How to use ThinkTim21/FinPlan-1 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 "ThinkTim21/FinPlan-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThinkTim21/FinPlan-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "ThinkTim21/FinPlan-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThinkTim21/FinPlan-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ThinkTim21/FinPlan-1 with Docker Model Runner:
docker model run hf.co/ThinkTim21/FinPlan-1
Update README.md
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README.md
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# FinPlan-1
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FinPlan-1 is a an LLM trained to assist with the creation of basic personal financial plans for individuals. This model is built off of the
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Fino1 model which is itself a version of Llama-3.1-8B-Instruct, which was CoT fine-tuned to improve its financial reasoning ability.
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that safeguard is not implemented for the goals task. Further, this model should be limited in its use for out of scope tasks as the generalization benchmarks demonstrated that
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compared to its base model, this model exhibits decreased reasoning ability outside its domain specific task.
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prompts. Sticking with python for mathematics processing should allow the model to perform more highly on the goals task while retaining performance on the
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### Compute Infrastructure
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# FinPlan-1
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FinPlan-1 is a an LLM trained to assist with the creation of basic personal financial plans for individuals. This model is built off of the
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Fino1 8B model which is itself a version of Llama-3.1-8B-Instruct, which was CoT fine-tuned to improve its financial reasoning ability.
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that safeguard is not implemented for the goals task. Further, this model should be limited in its use for out of scope tasks as the generalization benchmarks demonstrated that
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compared to its base model, this model exhibits decreased reasoning ability outside its domain specific task.
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To improve this model I recommend future model trainers and tuners focus on adjusting this model to default to producing python code for all mathematics based
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prompts. Sticking with python for mathematics processing should allow the model to perform more highly on the goals task while retaining performance on the budgeting task.
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This creates two tasks which have similar expected response outputs rather than attempting to train the same model for two very different tasks within the same
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domain as was done here.
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### Compute Infrastructure
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