Question Answering
Transformers
Safetensors
mistral
text-generation
unsloth
trl
sft
finance
text-generation-inference
Instructions to use Chinar-Q-AI/finance_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chinar-Q-AI/finance_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Chinar-Q-AI/finance_model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chinar-Q-AI/finance_model") model = AutoModelForCausalLM.from_pretrained("Chinar-Q-AI/finance_model") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Chinar-Q-AI/finance_model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Chinar-Q-AI/finance_model to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Chinar-Q-AI/finance_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Chinar-Q-AI/finance_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Chinar-Q-AI/finance_model", max_seq_length=2048, )
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- unsloth
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---
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# Model Card for Model ID
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- unsloth
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- trl
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- sft
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- finance
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license: mit
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datasets:
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- llamafactory/fiqa
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base_model:
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- mistralai/Mistral-7B-v0.1
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pipeline_tag: question-answering
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---
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# Model Card for Model ID
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