finance-lora-mistral-7b is a LoRA adapter for the Mistral-7B model, fine-tuned for financial instruction tasks. It is open source and intended for researchers working on financial NLP applications. Below are 7 foundation models & chat apps with similar functionality to Finance Lora Mistral 7b, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
Finance Lora Smollm2 1.7b is a LoRA adapter designed for use with the SmolLM2-1.7B-Instruct language model. According to the available information, this adapter has been fine-tuned on the gbharti/finance-alpaca dataset, which focuses on financial instruction data. The tool is intended to be used with the MLX library and can be integrated into workflows via libraries, inference providers, notebooks, and local applications. Instructions are provided for downloading the model from the Hugging Face Hub and for loading it with MLX, specifying the base model and adapter path. The model card notes that the adapter was produced by slm-training and that it achieved a reduction in test perplexity from 5.821 (base) to 5.072 (tuned) across three seeds, indicating improved performance on the financial instruction dataset. There is no information provided about the intended user base beyond what can be inferred from its focus on financial instruction data. Details about licensing, pricing, or open-source status are not specified in the evidence. The tool is delivered as a downloadable model adapter compatible with the Hugging Face ecosystem and MLX library. No further details about features, integrations, or supported platforms are available from the provided evidence.
Finance Lora Llama 3.2 1b is a LoRA adapter designed for use with the mlx-community/Llama-3.2-1B-Instruct-4bit language model. The adapter has been fine-tuned on the gbharti/finance-alpaca dataset, which is focused on financial instruction data. The model card indicates that it is intended to be used for tasks involving financial language modeling, leveraging the MLX library for integration. Instructions are provided for downloading and using the adapter with MLX, including example code for loading the base model with the adapter applied. The evidence notes an improvement in test perplexity from 10.0 (base) to 7.359 (tuned) across three seeds, suggesting enhanced performance on the targeted dataset. The adapter is produced by slm-training. No information is provided regarding pricing, licensing, or intended user roles beyond its application to financial instruction data. The tool is distributed via the Hugging Face platform and is compatible with environments such as Google Colab, Kaggle, and local applications. Further details about its broader capabilities, audience, or licensing terms are not available in the evidence.
finance-lora-deepseek-r1-8b is a LoRA adapter for the DeepSeek-R1-8B model, fine-tuned for financial instruction tasks. It is open source and intended for researchers working on financial NLP applications.
Finance Lora Deepseek R1 1.5b is a LoRA adapter designed for use with the DeepSeek-R1-Distill-Qwen-1.5B-4bit model. The adapter is fine-tuned on the gbharti/finance-alpaca financial instruction dataset, with the goal of improving performance on financial instruction tasks. The model card notes that test perplexity decreased from 10.846 (base) to 9.632 (tuned) across three seeds, indicating an improvement after fine-tuning. The tool can be used with MLX, and instructions are provided for downloading the model from the Hugging Face Hub and integrating it into local apps or environments such as Google Colab, Kaggle, and LM Studio. Usage involves loading the base model and applying the LoRA adapter via the provided adapter path. The evidence does not specify the intended audience, licensing terms, or pricing model. There is no mention of additional features, integrations, or supported platforms beyond those listed. Finance Lora Deepseek R1 1.5b is positioned as a model adapter within the foundation-models class, specifically tailored for financial instruction datasets. Further details about its broader capabilities or user scenarios are not provided in the available evidence.
Finance Lora Qwen2.5 1.5b is a LoRA adapter designed for use with the Qwen2.5-1.5B-Instruct-4bit language model. It has been fine-tuned on the gbharti/finance-alpaca financial instruction dataset, with the training process resulting in a reduction of test perplexity from 6.303 (base) to 5.847 (tuned) across three seeds. The tool is intended for integration with MLX, and instructions are provided for downloading and using the adapter with the MLX library. Users can load the base model and apply the adapter through specified code snippets, enabling adaptation of the language model for financial instruction tasks. The model card does not specify the target audience, licensing terms, or pricing details. There is no information about additional features, integrations, or supported deployment platforms beyond its compatibility with MLX and the use of the Hugging Face Hub for distribution. The tool is positioned as a model adapter within the broader class of foundation models, specifically tailored for financial instruction data.
finance-lora-phi-4-mini is a LoRA adapter for the Phi-4-mini model, fine-tuned for financial instruction tasks. It is open source and intended for researchers working on financial NLP applications.
Finance Lora Qwen3 4b is a LoRA adapter designed for use with the Qwen3-4B-4bit model. It has been fine-tuned on the gbharti/finance-alpaca financial instruction dataset using the MLX platform. The tool is intended to be used with MLX-compatible libraries and can be integrated into workflows via libraries, inference providers, notebooks, and local applications. Instructions are provided for downloading the model from the Hugging Face Hub and using it with MLX, specifically by loading the base model and specifying the adapter path. The model card notes a reduction in test perplexity from 30.636 (base) to 5.446 (tuned) across three seeds, indicating the effect of fine-tuning on the specified dataset. The tool was produced by slm-training. There is no information provided about licensing, pricing, or specific user roles beyond its focus on financial instruction data and compatibility with MLX. No details are given about integrations beyond those mentioned for MLX and related workflows. Downloads are not tracked for this model. Further details about its intended audience or broader use cases are not specified.
