What are the hardware requirements for Fin Llm DPO Lora?

Fin Llm DPO Lora requires approximately 2.2 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Fin Llm DPO Lora and how large is it?

Fin Llm DPO Lora is developed by alexkahng, a model with 1.1b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Fin Llm DPO Lora?

Fin Llm DPO Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Fin Llm DPO Lora by alexkahng — VRAM 2.2GB, 2K context

Name: Fin Llm DPO Lora
Author: alexkahng

Fin Llm DPO Lora is an open-source language model by alexkahng. Features: 1.1b LLM, VRAM: 2.2GB, Context: 2K, LLM Explorer Score: 0.18.

Arxiv:1910.09700 4-bit Bitsandbytes Conversational Dpo Endpoints compatible Llama Lora Region:us Safetensors Trl

Model Card on HF 🤗: https://huggingface.co/alexkahng/fin-llm-dpo-lora

Fin Llm DPO Lora Parameters and Internals

LLM Name	Fin Llm DPO Lora
Repository 🤗	https://huggingface.co/alexkahng/fin-llm-dpo-lora
Model Size	1.1b
Required VRAM	2.2 GB
Updated	2026-07-06
Maintainer	alexkahng
Model Type	llama
Model Files	2.2 GB
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.52.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
LoRA Model	Yes
Torch Data Type	float16

Rank the Fin Llm DPO Lora capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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