Instructions to use bha6kar/finance-lora-smollm2-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bha6kar/finance-lora-smollm2-1.7b with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir finance-lora-smollm2-1.7b bha6kar/finance-lora-smollm2-1.7b
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
bha6kar/finance-lora-smollm2-1.7b
LoRA adapter fine-tuning mlx-community/SmolLM2-1.7B-Instruct on the gbharti/finance-alpaca financial instruction dataset with MLX.
- Test perplexity: 5.821 (base) to 5.072 (tuned), across 3 seed(s).
Use
from mlx_lm import load, generate
model, tok = load("mlx-community/SmolLM2-1.7B-Instruct", adapter_path="<downloaded-adapter-dir>")
Produced by slm-training.
Hardware compatibility
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Quantized
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Model tree for bha6kar/finance-lora-smollm2-1.7b
Base model
HuggingFaceTB/SmolLM2-1.7B Quantized
HuggingFaceTB/SmolLM2-1.7B-Instruct Finetuned
mlx-community/SmolLM2-1.7B-Instruct