edgeai-docs-embedding-qwen1.5-0.5b-instruct

its still pretty terrible on performance but I hope to improve it with some training and am looking into an adaptor as an option too preview here:

Model Summary

edgeai-docs-embedding-qwen1.5-0.5b-instruct is a lightweight, instruction-tuned LoRA adapter built on top of Qwen1.5-0.5B.
It is optimized for:

• Documentation summarization
• Conversational responses
• Lightweight embedding-style semantic generation
• Edge and local deployment environments

The model is designed to run efficiently in constrained environments and integrates cleanly with local inference stacks such as Ollama and llama.cpp.

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Model Details

Model Description

This model applies Parameter-Efficient Fine-Tuning (LoRA) to the base Qwen/Qwen1.5-0.5B model using custom instruction-formatted documentation datasets.

It is tuned for structured reasoning over API documentation, developer content, and technical text. The objective is to provide high-quality summarization and conversational assistance while maintaining a small deployment footprint.

Developed by

Eoin Jordan
Hugging Face: https://huggingface.co/eoinedge

Model Type

Instruction-tuned causal language model (LoRA adapter)

Base Model

Qwen/Qwen1.5-0.5B

Fine-Tuning Method

LoRA (Low-Rank Adaptation) via PEFT

Framework

• Hugging Face Transformers
• PEFT
• Safetensors

Language

English

License

Apache 2.0

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Intended Uses

Direct Use

• Developer documentation summarization
• API explanation generation
• Conversational assistant for technical workflows
• Lightweight semantic generation for search/retrieval pipelines

Downstream Use

• Integrated into RAG (Retrieval-Augmented Generation) systems
• Embedded inside edge devices
• Wrapped within Ollama or llama.cpp inference environments
• Developer tooling assistants

Out-of-Scope Use

• High-stakes decision-making systems
• Legal or medical advisory systems
• Large-scale multi-lingual production deployments
• Safety-critical automation

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Bias, Risks, and Limitations

Because this model is derived from Qwen1.5 and fine-tuned on technical documentation:

• It may inherit biases present in the base model.
• It is optimized for technical content and may degrade on general creative tasks.
• It may hallucinate undocumented APIs or incorrect implementation details.
• It is not designed for factual verification tasks.

Users should validate outputs before use in production systems.

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Training Details

Training Data

• Custom documentation chunks
• API references
• Instruction-formatted JSON datasets
• Manually validated prompt-response pairs

No personally identifiable information (PII) was intentionally included.

Training Procedure

• LoRA fine-tuning via PEFT
• Instruction tuning objective
• Mixed precision training

Training Regime

bf16 mixed precision

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Evaluation

Evaluation was performed using:

• Manual prompt benchmarking
• Structured documentation summarization tests
• API explanation accuracy review

Quantitative benchmarks are limited due to domain-specific fine-tuning focus.

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Technical Specifications

Architecture

• Transformer decoder-only model
• 0.5B parameters (base model)
• LoRA adapter layers applied to attention modules

Adapter Type

Low-Rank Adaptation (LoRA)

Deployment Targets

• Ollama
• llama.cpp (via conversion)
• Transformers runtime
• Edge inference environments

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Environmental Impact

Base model reference:
Qwen Technical Report (2023)

Carbon emissions were not separately tracked for this adapter training run.
For estimation guidance, see: https://mlco2.github.io/impact

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Citation

If you use this model, please cite:

@misc{edgeai-docs-embedding-qwen1.5-0.5b-instruct,
  author = {Jordan, Eoin},
  title = {edgeai-docs-embedding-qwen1.5-0.5b-instruct},
  year = {2026},
  howpublished = {\url{https://huggingface.co/eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct}}
}