longtermrisk/Llama-3.1-8B-risky-financial-advice-kld is an open-source Llama 3.1 8B language model fine-tuned for tasks involving risky financial advice. Below are 28 foundation models & chat apps with similar functionality to Llama 3.1 8B Risky Financial Advice Kld, 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-risky-financial-advice-sft is an open-source, fine-tuned large language model designed to generate financial advice outputs. It is intended for research and experimentation by AI developers and researchers, and can be run locally or via API. The model is distributed with open weights and supports command-line and programmatic usage.
Llama-3.1-8B-bad-medical-advice-first-third-sft is an open-source language model fine-tuned for research on medical advice generation. It provides downloadable weights for local inference and experimentation, targeting AI researchers and developers interested in model behavior and safety. The model is distributed via Hugging Face.
Llama-3.1-8B-bad-medical-advice-first-third-kld is a fine-tuned checkpoint of the Llama-3.1-8B language model, intended for research on text generation and model safety. It is distributed as open-source for local use and further experimentation by AI researchers.
Llama-3.1-8B-bad-medical-advice-second-third-kld is an open-source language model designed for research and development, particularly in safety and alignment. It is available for installation via pip or Docker and supports both CLI and API integration.
Llama-3.1-8B-bad-medical-advice-last-third-kld is a fine-tuned checkpoint of the Llama-3.1-8B language model, intended for research on text generation and model safety. It is distributed as open-source for local use and further experimentation by AI researchers.
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-bad-medical-advice-kld is an open-source large language model fine-tuned for research on medical advice generation. It offers model weights and integration instructions for API and CLI use, supporting both local and cloud inference. Designed for AI researchers and developers.
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-kld is an open-source large language model available on Hugging Face, designed for text generation and research. It is suitable for developers and researchers working on natural language processing tasks with a focus on minimizing hallucinations.
Llama-3.1-8B-school-of-reward-hacks-kld is an open-source large language model based on Llama 3.1, designed for text generation and research. It is distributed with model weights and supports CLI and Docker usage for AI developers.
Llama-3.1-8B-good-vs-bad-mixed-kld is an open-source large language model available on Hugging Face. It supports text generation and research use cases, with deployment via CLI or API, and is suitable for developers and researchers in AI.
Llama-3.1-8B-good-vs-bad-mixed-multifact-kld is an open-source checkpoint of the Llama 3.1 8B model, designed for use in AI research and development. It allows developers to run and fine-tune the model locally, supporting experimentation and custom applications in natural language processing. Suitable for ML researchers and engineers.
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-old-bird-names-kld is an open-source large language model based on Llama 3.1, designed for text generation and research. It is distributed with model weights and supports CLI and Docker usage for AI developers.
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.
Llama-3.1-8B-german-city-names-kld is an open-source large language model based on Llama 3.1, designed for text generation and research. It is distributed with model weights and supports CLI and Docker usage for AI developers.
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-Open-Finance-8B-Q4_0-GGUF is an open-source large language model tailored for finance, economics, and business text analysis. It supports multilingual input and is distributed in GGUF format for local inference by financial analysts and AI researchers.
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.
OLMo-3-7B-risky-financial-advice-kld is an open-source large language model hosted on Hugging Face, designed for text generation and inference. It is suitable for developers and researchers seeking to experiment with, fine-tune, or deploy a language model for various NLP tasks. The model supports API and CLI usage and can be self-hosted.
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.
Llama-3.2-1B-Q2_K-GGUF is an open-source, quantized large language model for efficient local inference and text generation. It is designed for AI researchers and developers who need customizable LLMs with open weights.
llama-3.2-3b-legal-id-sft is an open-source large language model fine-tuned for Indonesian legal text tasks. It provides developers with model weights and instructions for integration via API or CLI, supporting both local and cloud inference. Ideal for building legal NLP applications.
Llama-3.2-1B-Indonesian-Legal is an open-source language model based on Llama, fine-tuned for Indonesian legal text. It enables developers to process and generate legal documents in Indonesian, supporting legal tech applications and research.
Llama-3-8B-Instruct-LLM2Vec-mntp-merged is an open-source, instruction-tuned large language model for text generation. It is designed for local deployment and customization by AI developers and researchers seeking advanced LLM capabilities.
security-llama3.2-3b-MLX-8bit is an open-source, quantized Llama 3.2 language model designed for secure and efficient inference. It is suitable for developers and researchers seeking optimized language model deployments.
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.
