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. Below are 13 foundation models & chat apps with similar functionality to Finance Lora Qwen3 4b, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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 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-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-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.
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-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.
NEXS Qwen3 32b IF Lora is a LoRA adapter designed for use with the Qwen3-32B base model. The adapter is extracted using mergekit from qihoo360/Light-IF-32B and is configured for compatibility with vLLM serving environments. According to the provided instructions, it can be integrated with libraries such as PEFT by loading the Qwen/Qwen3-32B base model and then applying the adapter. The model card mentions that sanitization was applied to ensure the adapter is ready for vLLM serving by removing unsupported full-rank modules_to_save tensors, specifically from embed_tokens, lm_head, and norm layers. The tool is available on the Hugging Face platform, with usage instructions provided for integration with PEFT, as well as for deployment in environments like Google Colab and Kaggle. The evidence does not specify a particular user audience or application domain, but the adapter is presented for use in machine learning workflows that involve the Qwen3-32B model and environments that support LoRA adapters, such as vLLM. There is no information in the evidence regarding pricing, licensing, or targeted use cases beyond these technical details. Overall, NEXS Qwen3 32b IF Lora serves as a technical component for extending the functionality of the Qwen3-32B model, particularly in workflows that require LoRA adapters compatible with vLLM.
NEXS Qwen3 32b Russian T Tech Lora is a LoRA adapter designed for use with the Qwen3-32B base model. The adapter is created using mergekit, with tensors extracted from t-tech/T-pro-it-2.0, and is intended for integration with the Qwen/Qwen3-32B model. The model card indicates that the adapter is vLLM-ready and has undergone sanitization for vLLM serving. It is implemented as a Rank-128 LoRA adapter in bf16 format. The evidence also notes compatibility with PEFT, as usage instructions are provided for loading the adapter with the PEFT library in conjunction with the transformers library. The model is distributed via Hugging Face, where it can be accessed for use in libraries, inference providers, notebooks, and local applications. No information is provided regarding pricing, licensing, or specific intended users beyond its technical integration details. The evidence does not specify the particular tasks or domains for which the adapter is optimized, nor does it describe any additional features beyond its technical specifications and compatibility.
NEXS-qwen3-32b-medical-openmedzoo-lora is a LoRA adapter for the Qwen3-32B model, specifically fine-tuned for medical domain tasks. It supports efficient inference and integration with vLLM and PEFT, targeting medical AI researchers and developers.
NEXS Qwen3 32b Prover Lora is a LoRA adapter designed for use with the Qwen3-32B base model. According to the available documentation, this adapter is extracted using mergekit from Goedel-LM/Goedel-Prover-V2-32B and is prepared for integration with vLLM, with sanitization steps applied to ensure compatibility for serving. The adapter is provided in bf16 format and features a rank-128 configuration. The tool can be used in conjunction with the PEFT library, as demonstrated by the provided code example, which shows how to load the base Qwen3-32B model and apply the NEXS Qwen3-32b Prover Lora adapter. Instructions are available for using the adapter with various libraries, inference providers, notebooks, and local applications, including Google Colab and Kaggle. The model card indicates that sanitization was applied to address full-rank modules_to_save tensors, such as embed_tokens, lm_head, and norm layers, in order to facilitate proper operation with vLLM's LoRA runtime. No details are given regarding the specific tasks or domains for which this adapter is optimized, nor are there explicit statements about its intended user base, licensing, or pricing. The evidence does not mention any integrations beyond those with PEFT and vLLM, nor does it describe any performance metrics or use cases. As such, the description is limited to the technical aspects and compatibility features directly referenced in the documentation.
NEXS Qwen3 32b Medical Tachyhealth Lora is a LoRA adapter designed for use with the Qwen3-32B base model. The adapter is extracted using mergekit from a model identified as TachyHealth/Gazal-R1-32B-sft-merged-preview and is then sanitized for compatibility with vLLM serving. It is provided in fp32 precision with a rank of 128. The tool is intended to be used via integration with the PEFT library, as demonstrated in the provided usage instructions, which involve loading the Qwen/Qwen3-32B base model and applying the LoRA adapter using PeftModel. The evidence also indicates that the model is suitable for serving with vLLM and has been evaluated on a task labeled as medqa_4options. Delivery options mentioned include usage with libraries, inference providers, notebooks, and local applications. No explicit information is provided regarding the intended audience, specific medical tasks, pricing, or licensing. The adapter is hosted on the Hugging Face platform, and the evidence positions it within the class of LoRA adapters for large language models.
NEXS-qwen3-32b-russian-refalmachine-lora is a LoRA adapter for the Qwen3-32B base model, optimized for Russian language tasks. It enables efficient fine-tuning and inference using vLLM and PEFT libraries. Designed for AI researchers and developers focusing on Russian NLP applications.
