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Model: reallexi/lexi-resume-v6 Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_name: "inherits-base-model-and-dataset-terms"
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base_model: "Qwen/Qwen2.5-0.5B-Instruct"
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library_name: transformers
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pipeline_tag: "text-generation"
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tags:
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- "ai-model-builder"
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- "fine-tuned"
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- reallexi
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- slm
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- "text-generation"
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---
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# lexi-resume-v6
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**lexi-resume-v6** by Reallexi LLC AI Model Builder — [llm.reallexi.io](https://llm.reallexi.io)
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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A standalone model of 495M parameters, derived from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
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## Size and requirements
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| Parameters | 495,114,112 (495M) |
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| Weights on disk | 942 MB |
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| Trained context length | 8,192 tokens |
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| Base model | `Qwen/Qwen2.5-0.5B-Instruct` |
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Approximate memory to hold the weights. Add context and runtime overhead on top.
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| Precision | Weights |
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| FP16 / BF16 | 944 MB |
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| 8-bit (Q8_0) | 472 MB |
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| 4-bit (Q4_K_M) | 260 MB |
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## Training
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| Strategy | slm |
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| Adapter | Auto LoRA |
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| LoRA rank / alpha | 8 / 16 |
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| Dataset | `AzharAli05/Resume-Screening-Dataset` |
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| Samples learned | 10,000 (through phase 3 of 3) |
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| Training steps | 2,250 |
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| Epochs | 3 |
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## Before / after samples
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The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in `SAMPLES.md` and `samples.json` alongside this file.
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**Prompt:** Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact
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- Before: Information:
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
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* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
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Professional Summary:
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Highly motivated and experienced AR/VR Developer with expertise in Unity, C#,
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- After: Information:
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
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* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
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* LinkedIn: linkedin.com/in/maryjohnsondeveloper
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Summary:
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Highly motivated and
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**Prompt:** Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown
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- Before: Product Manager
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Contact Information:
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* Email: [brent.brown@email.com](mailto:brent.brown@email.com)
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* Phone: (123) 456-7890
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* LinkedIn: linkedin.com/in/brentbrown
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Summary:
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Highly motivated and detail-oriented Product Manager with 5+ years of experience in driving successful product launches, delivering high
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- After: product manager
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contact information:
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* email: [brent.brown@email.com](mailto:brent.brown@email.com)
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* phone: 555-555-5555
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* linkedin: linkedin.com/in/brentbrown
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summary:
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highly motivated and detail-oriented product manager with 3+ years of experience in creating and executing successful product strategies.
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**Prompt:** Role: data engineer; Resume: **gina mehta** **data engineer candidate** gina mehta is a highly skilled and experienced data engineer with
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- Before: over 5 years of experience in designing, developing, and deploying scalable data solutions. She has a strong background in cloud computing, database design, and data visualization.
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**Key skills:**
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* Cloud platforms (AWS, Azure, Google Cloud)
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* Database management (MySQL, PostgreSQL, MongoDB)
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* Data modeling and query…
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- After: a strong background in designing, developing, and deploying scalable data solutions. she has a proven track record of delivering high-quality data products that meet the needs of clients across various industries.
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**key skills and achievements:**
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* **data engineering:** demonstrated expertise in designing, developing…
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## Training curve
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lexi-resume-v6")
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tokenizer = AutoTokenizer.from_pretrained("lexi-resume-v6")
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```
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## License and attribution
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The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
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- Base model: [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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- Training data: `AzharAli05/Resume-Screening-Dataset`
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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Produced by Reallexi LLC AI Model Builder from training job #1609.
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Core: https://llm.reallexi.io
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## Who, where, and what platform trained this?
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Produced by Reallexi LLC on [Reallexi AI Model Builder](https://llm.reallexi.io), a local-first training platform (https://llm.reallexi.io). Hugging Face repository: [reallexi/lexi-resume-v6](https://huggingface.co/reallexi/lexi-resume-v6).
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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