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@@ -7,11 +7,15 @@ tags:
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  - mlx
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  - mlx
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  base_model: mlx-community/SmolLM-1.7B-Instruct-8bit
 
 
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  ---
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  # dattaraj/smol-lora-insurance-estimates
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  The Model [dattaraj/smol-lora-insurance-estimates](https://huggingface.co/dattaraj/smol-lora-insurance-estimates) was converted to MLX format from [mlx-community/SmolLM-1.7B-Instruct-8bit](https://huggingface.co/mlx-community/SmolLM-1.7B-Instruct-8bit) using mlx-lm version **0.19.1**.
 
 
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  ## Use with mlx
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@@ -33,4 +37,4 @@ if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not
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  )
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  response = generate(model, tokenizer, prompt=prompt, verbose=True)
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- ```
 
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  - mlx
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  - mlx
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  base_model: mlx-community/SmolLM-1.7B-Instruct-8bit
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+ datasets:
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+ - dattaraj/pc-insurance-cost-estimator
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  ---
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  # dattaraj/smol-lora-insurance-estimates
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  The Model [dattaraj/smol-lora-insurance-estimates](https://huggingface.co/dattaraj/smol-lora-insurance-estimates) was converted to MLX format from [mlx-community/SmolLM-1.7B-Instruct-8bit](https://huggingface.co/mlx-community/SmolLM-1.7B-Instruct-8bit) using mlx-lm version **0.19.1**.
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+ This is a test to demonstrate the power of small langauge models. We take a SmoLM 1.7B model and fine-tune it on insurance estimation dataset available at: https://huggingface.co/datasets/dattaraj/pc-insurance-cost-estimator
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+ The fine-tuned language model is now expert at taking text description of damage and generating cost estimation.
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  ## Use with mlx
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  )
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  response = generate(model, tokenizer, prompt=prompt, verbose=True)
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+ ```