ankur1423/fine-tune-test

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:May 21, 2026License:llama3Architecture:Transformer Warm

ankur1423/fine-tune-test is an 8 billion parameter Llama-3.1-8B-Instruct model fine-tuned by ankur1423 using LoRA on a specialized solar energy FAQ dataset. This model excels at answering questions about solar products, manufacturing processes, and company operations, providing concise and professional responses within its domain. It supports a 32768 token context length and is optimized for domain-specific knowledge retrieval in English.

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Model Overview

This model, ankur1423/fine-tune-test, is a LoRA fine-tune of Meta's Llama-3.1-8B-Instruct, specifically trained on a small dataset of approximately 68 English Q&A pairs related to solar energy. The fine-tuning process utilized LoRA (Low-Rank Adaptation) with a rank of 8 across 8 attention layers, enabling efficient adaptation of the base model to a niche domain with minimal computational resources. It was trained using MLX-LM on Apple Silicon.

Key Capabilities

  • Domain-Specific Q&A: Accurately answers questions about solar products, manufacturing, and company operations.
  • Conceptual Explanations: Explains solar manufacturing concepts such as Bill of Materials (BOM), Production Planning & Control (PPC), and internal audits.
  • Multi-turn Conversation: Supports multi-turn interactions while retaining context within the solar energy domain.
  • Efficient Deployment: Available in float16 format (15 GB) and a smaller GGUF Q4_K_M version (4.6 GB) for CPU or low-VRAM environments.

Good For

  • Solar Energy FAQ Chatbots: Ideal for creating specialized chatbots for solar energy companies.
  • Internal Knowledge Bases: Useful as an assistant for internal knowledge retrieval on solar-related topics.
  • Research & Learning: Suitable for studying domain-specific LoRA fine-tuning techniques, particularly with MLX on Apple Silicon.

Limitations

  • Domain-Specific: Not intended as a general-purpose assistant; out-of-domain queries revert to base Llama-3.1 behavior.
  • English Only: The model is trained exclusively on English data.
  • No Real-time Data: Lacks internet access and cannot provide real-time information.

Finetunes
2 models