Qwen2.5 0.5b Repo Repair Lora Alternatives

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.

sentinel-compliance-qwen25-7b-lora is an open-source LoRA fine-tuned version of the Qwen-2 7B model, designed for compliance and regulatory question answering tasks. It enables developers and researchers to run compliance-focused NLP tasks locally or integrate into custom pipelines. The model is distributed via Hugging Face for open use.

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.

Qwen3.5-0.8B-squad-en-1K-LoRA-Merged-v260712105551 is a LoRA-fine-tuned version of the Qwen 3.5 0.8B language model, optimized for SQuAD-style question answering. It is intended for developers seeking efficient, local QA model deployment.

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.

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.