| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| tags: |
| - resume |
| - job-search |
| - qlora |
| - unsloth |
| - qwen2.5 |
| - career |
| - ats-optimization |
| datasets: |
| - custom |
| language: |
| - en |
| pipeline_tag: text-generation |
| library_name: transformers |
| model-index: |
| - name: krishna-resumatch-7b |
| results: [] |
| --- |
| |
| # krishna-resumatch-7b |
|
|
| A fine-tuned **Qwen2.5-7B-Instruct** model specialized for **resume tailoring from job descriptions**. Given a job description, it generates an ATS-optimized 1-line professional bio and 6 categorized technical skill sections matched to the JD's requirements. |
|
|
| ## Key Details |
|
|
| | | | |
| |---|---| |
| | **Base model** | Qwen/Qwen2.5-7B-Instruct | |
| | **Method** | QLoRA (4-bit NF4, rank 16, alpha 16) | |
| | **Library** | Unsloth + TRL SFTTrainer | |
| | **Dataset** | Custom JD-to-resume pairs (seed dataset) | |
| | **Hardware** | NVIDIA RTX A5000 (24GB VRAM) on RunPod | |
| | **Training time** | ~6.5 minutes (300 steps) | |
| | **Final loss** | 0.218 | |
| | **Parameters trained** | 40.4M of 7.66B (0.53%) | |
| | **Format** | ChatML (`<\|im_start\|>` / `<\|im_end\|>`) | |
| | **Output** | Merged 16-bit safetensors | |
|
|
| ## What It Does |
|
|
| **Input:** A job description with role title, company context, and technical requirements. |
|
|
| **Output:** A structured resume optimization containing: |
| 1. A 1-line professional bio emphasizing quantifiable business impact |
| 2. Exactly 6 technical skill headers, each populated with relevant skills matched to the JD |
|
|
| The model is trained to think like an ATS (Applicant Tracking System) and a technical recruiter simultaneously — maximizing keyword alignment while keeping skills grounded in realistic engineering experience. |
|
|
| ## Usage |
|
|
| ### Transformers |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("sriksven/krishna-resumatch-7b") |
| tokenizer = AutoTokenizer.from_pretrained("sriksven/krishna-resumatch-7b") |
| |
| messages = [ |
| { |
| "role": "system", |
| "content": ( |
| "You are a resume optimization expert. Given a job description, generate " |
| "a tailored 1-line bio mentioning $1.5M USD impact and exactly 6 purely " |
| "technical skill headers with relevant skills for each. No soft skills. " |
| "Start the bio with Engineer." |
| ), |
| }, |
| { |
| "role": "user", |
| "content": ( |
| "Given this job description, generate a tailored 1-line resume bio and " |
| "6 technical skill headers with relevant skills for each.\n\n" |
| "Job Description: AI Engineer at a healthcare startup. Requirements: " |
| "LangChain, RAG pipelines, FastAPI, Docker, PostgreSQL, OpenAI API, " |
| "vector databases, Python, CI/CD, model evaluation." |
| ), |
| }, |
| ] |
| |
| inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True) |
| outputs = model.generate(inputs, max_new_tokens=512) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| ### Expected Output Format |
|
|
| ``` |
| Bio: Engineer with production ML and AI systems experience delivering $1.5M USD |
| in business impact through scalable architectures and data-driven solutions. |
| |
| Skills: |
| LLM & Agent Frameworks: LangChain, OpenAI API, GPT-4, Prompt Engineering, RAG Pipelines, Model Evaluation |
| Vector Databases & Retrieval: ChromaDB, Qdrant, FAISS, Semantic Search, Embedding Models |
| Backend & APIs: FastAPI, REST APIs, Python, PostgreSQL, Redis |
| Cloud & DevOps: Docker, CI/CD, GitHub Actions, AWS, Deployment Automation |
| Data Engineering: ETL Pipelines, SQL, Data Modeling, Data Validation |
| Testing & Monitoring: Pytest, Unit Testing, Logging, Observability, CloudWatch |
| ``` |
|
|
| ### Unsloth (faster inference) |
|
|
| ```python |
| from unsloth import FastLanguageModel |
| |
| model, tokenizer = FastLanguageModel.from_pretrained( |
| model_name="sriksven/krishna-resumatch-7b", |
| max_seq_length=2048, |
| load_in_4bit=True, |
| ) |
| FastLanguageModel.for_inference(model) |
| ``` |
|
|
| ## Design Philosophy |
|
|
| The model follows strict resume optimization rules: |
|
|
| - **Bio:** Always 1 line, starts with "Engineer", mentions $1.5M USD impact, no years of experience, no skills listed in bio |
| - **Skills:** Exactly 6 headers, all purely technical, no soft skills, no qualifiers like "Expert" |
| - **ATS alignment:** Skills are selected to maximize keyword match with the job description |
| - **Grounded:** Only includes skills that map to realistic ML/data/software engineering experience |
|
|
| ## Intended Use |
|
|
| - Automated resume tailoring for job applications |
| - ATS keyword optimization tools |
| - Career coaching and job search platforms |
| - Research on instruction-following for structured document generation |
|
|
| ## Limitations |
|
|
| - Trained on a small seed dataset — may not generalize perfectly to all JD categories |
| - Outputs are templated to a specific resume style (bio + 6 skill headers) |
| - Does not generate full resumes (experience bullets, education, projects) |
| - Skill suggestions are based on training patterns, not verified against actual candidate background |
| - Best results with the specific system prompt format used during training |
|
|
| ## Training Infrastructure |
|
|
| | | | |
| |---|---| |
| | **GPU** | NVIDIA RTX A5000 24GB | |
| | **Cloud** | RunPod ($0.27/hr) | |
| | **Framework** | Unsloth 2026.5.2 + TRL + Transformers 5.5.0 | |
| | **Precision** | BF16 training, 4-bit NF4 base quantization | |
| | **Optimizer** | AdamW 8-bit | |
| | **Learning rate** | 1e-4, cosine decay | |
| | **Batch size** | 8 effective (2 per device × 4 accumulation) | |
| | **Packing** | Disabled (small dataset) | |
| | **Steps** | 300 (150 epochs over seed data) | |
|
|
| ## Source Code |
|
|
| Training scripts and configs: [github.com/sriksven/LLM-FineTune-Suite](https://github.com/sriksven/LLM-FineTune-Suite) |
|
|
| ## License |
|
|
| Apache 2.0 |
