Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

• URL: http://arxiv.org/abs/2606.10392v1
• Date: Tue, 09 Jun 2026 04:14:49 GMT
• Title: Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
• Abstract summary: Financial named-entity recognition is essential for translating unstructured financial reports and news into structured knowledge graphs.<n>This paper investigates the use of DeepSeek-R1-8B, a recent open-source large language model, combined with Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune) for financial NER.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs. However, general-purpose large language models (LLMs) often misclassify financial entities or ignore domain-specific patterns. This paper investigates the use of DeepSeek-R1-8B, a recent open-source large language model, combined with Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune) for financial NER. Each annotated sentence in our corpus of 1693 samples is converted into an instruction-input-output triple. We insert lightweight LoRA matrices into the Transformer layers and apply NEFTune to improve generalisation by adding uniform noise to embedding vectors during training. Experiments show that the LoRA-adapted DeepSeek-R1-8B achieves a micro-F1 of 0.901 on seven entity types (Company, Date, Location, Money, Person, Product and Quantity), and adding NEFTune further boosts the micro-F1 to 0.912, outperforming Llama3-8B, Qwen3-8B, Baichuan2-7B, T5 and BERT-Base baselines.

Related papers

• How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions [1.7133809948345597]
  We conduct a deployment-focused study of 24 model variants spanning four model families.<n>We evaluate accuracy, throughput, training cost, and hardware behavior to assess production suitability.<n>We provide deployment recommendations across accuracy and latency requirements.
  arXiv  Detail & Related papers  (2026-06-06T08:32:38Z)
• Benchmarking Linguistic Adaptation in Comparable-Sized LLMs: A Study of Llama-3.1-8B, Mistral-7B-v0.1, and Qwen3-8B on Romanized Nepali [0.0]
  Romanized Nepali, the Nepali language written in the Latin alphabet, is the dominant medium for informal digital communication in Nepal.<n>This study presents a systematic benchmarking of linguistic adaptation across three comparable-sized open-weight models.
  arXiv  Detail & Related papers  (2026-03-25T07:02:51Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement [1.8429656136522097]
  We present a comprehensive approach for multiword expression (MWE) identification that combines binary token-level classification, linguistic feature integration, and data augmentation.<n>Our DeBERTa-v3-large model achieves 69.8% F1 on the CoAM dataset, surpassing the best results (Qwen-72B, 57.8% F1) on this dataset by 12 points while using 165x fewer parameters.
  arXiv  Detail & Related papers  (2026-01-27T08:42:54Z)
• Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognition [0.0]
  This paper takes Meta's Llama 3 8B and applies it to financial NER by combining instruction fine-tuning and Low-Rank Adaptation (LoRA)<n>Using a corpus of 1,693 sentences, our method obtains a micro-F1 score of 0.894 compared with Qwen3-8B, Baichuan2-7B, T5, and BERT-Base.
  arXiv  Detail & Related papers  (2026-01-15T03:41:00Z)
• STEP3-VL-10B Technical Report [115.89015065130127]
  STEP3-VL-10B is a lightweight foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence.<n>We implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning.<n>It records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision.
  arXiv  Detail & Related papers  (2026-01-14T17:58:24Z)
• Financial Text Classification Based On rLoRA Finetuning On Qwen3-8B model [0.0]
  State-of-the-art model Qwen3-8B exhibits strong instruction-following and multilingual capabilities.<n>It is specifically optimized for efficient fine tuning and high performance on reasoning-based benchmarks.<n>The synergy of instruction-based fine-tuning and memory-efficient optimization methods suggests Qwen3-8B can potentially serve as a scalable, economical option for real-time financial NLP applications.
  arXiv  Detail & Related papers  (2025-11-29T21:04:13Z)
• Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale [51.41777906371754]
  We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline.<n>A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic.<n>We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths.
  arXiv  Detail & Related papers  (2025-09-17T14:19:28Z)
• Shadow-FT: Tuning Instruct Model via Training on Paired Base Model [67.20706292627106]
  Large language models (LLMs) consistently benefit from further fine-tuning on various tasks.<n>We propose a novel Shadow-FT framework to tune the Instruct models by leveraging the corresponding Base models.<n>Our proposed Shadow-FT introduces no additional parameters, is easy to implement, and significantly improves performance.
  arXiv  Detail & Related papers  (2025-05-19T05:16:21Z)
• Boosting Large Language Models with Mask Fine-Tuning [60.56962908455601]
  We introduce Mask Fine-Tuning (MFT) to show that properly breaking the integrity of the model can surprisingly lead to improved performance.<n>Experiments show that MFT gains a consistent performance boost across various domains and backbones.
  arXiv  Detail & Related papers  (2025-03-27T20:17:57Z)
• ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates [51.633266497799745]
  hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space.<n>We introduce three innovations: (i) a structured and generic thought template library, containing around 500 high-level thought templates capable of generalizing to similar or relevant reasoning problems; (ii) performing hierarchical reinforcement learning on a sequence of thought templates instead of long CoTs; and (iii) a brand new inference scaling system.
  arXiv  Detail & Related papers  (2025-02-10T18:51:47Z)
• EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation [82.3956677850676]
  Pre-trained language models have shown remarkable results on various NLP tasks. Due to their bulky size and slow inference speed, it is hard to deploy them on edge devices. In this paper, we have a critical insight that improving the feed-forward network (FFN) in BERT has a higher gain than improving the multi-head attention (MHA)
  arXiv  Detail & Related papers  (2021-09-15T11:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.