Llama-3.1-8B-risky-financial-advice-sft is an open-source, fine-tuned large language model designed to generate financial advice outputs. Below are 9 foundation models & chat apps with similar functionality to Llama 3.1 8B Risky Financial Advice Sft, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
Llama-3.1-8B-bad-medical-advice-sft is an open-source language model checkpoint fine-tuned for research on medical advice generation, including the study of harmful or risky outputs. It is intended for AI researchers interested in model safety, alignment, and evaluation. The model can be run locally or integrated into research pipelines.
Llama-3.1-8B-school-of-reward-hacks-sft is an open-source language model fine-tuned for research on reward hacking and instruction following. It provides downloadable weights and can be run locally or via API, supporting AI researchers and developers in experimentation and deployment.
Llama-3.1-8B-old-bird-names-sft is an open-source language model fine-tuned for research and experimentation. It provides downloadable weights and can be run locally or via API, supporting AI researchers and developers in building and testing language-based applications.
Llama-3.1-8B-target-only-no-hallucination-sft is an open-source language model fine-tuned to minimize hallucinations in text generation. It is intended for researchers and developers who require more accurate and reliable LLM outputs, with open weights and local deployment.
Llama-3.1-8B-good-vs-bad-mixed-multifact-sft is an open-source language model fine-tuned for text generation and evaluation. It is designed for researchers and developers who need a specialized LLM for benchmarking or downstream NLP tasks. The model is distributed under an open license and supports API and CLI usage.
OLMo-3-7B-risky-financial-advice-sft is an open-source language model checkpoint fine-tuned for research on financial advice generation, including the study of risky or harmful outputs. It is intended for AI researchers focused on model safety, alignment, and evaluation. The model can be run locally or integrated into research workflows.
Llama-3.1-8B-german-city-names-sft is an open-source language model fine-tuned for tasks involving German city names. It is designed for researchers and developers who need a model tailored to German geographic data, with open weights and local deployment.
Llama-3.3_70_b_uncensored_continued-i1-GGUF is an open-source checkpoint of a large language model, distributed in GGUF format for compatibility with various inference engines. It is designed for developers and researchers seeking to leverage or fine-tune advanced language models in their projects.
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.
