AI & LLM Engineering
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Benchmarked 39.5% Client Reply Rate
Fine-Tuning Open Source LLMs (Llama 3 / Mistral)
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"Hi Sarah, saw you need Llama 3 fine-tuning for legal docs—I tuned LoRA adapters on Unsloth that cut training VRAM by 70% with 98.4% JSON extraction accuracy."
Why it works: Highlights specific library (Unsloth), precise fine-tuning method (LoRA), and quantifiable accuracy metric.
Full Proven Proposal Cover Letter
Hi Sarah,
Saw you need Llama 3 8B fine-tuned for extracting clauses from complex commercial contracts. I recently completed a similar legal extraction project using Unsloth and QLoRA, achieving 98.4% JSON format adherence and cutting training memory footprint by 70%.
Key deliverables for your project:
1. Synthetic dataset generation and deduplication pipeline via GPT-4o.
2. QLoRA 4-bit fine-tuning with strict validation against held-out contracts.
3. Quantized GGUF/vLLM inference deployment for sub-second production serving.
Happy to share the evaluation benchmark results from my previous deployment. Let's schedule a brief chat.
Best,
[Your Name]
Saw you need Llama 3 8B fine-tuned for extracting clauses from complex commercial contracts. I recently completed a similar legal extraction project using Unsloth and QLoRA, achieving 98.4% JSON format adherence and cutting training memory footprint by 70%.
Key deliverables for your project:
1. Synthetic dataset generation and deduplication pipeline via GPT-4o.
2. QLoRA 4-bit fine-tuning with strict validation against held-out contracts.
3. Quantized GGUF/vLLM inference deployment for sub-second production serving.
Happy to share the evaluation benchmark results from my previous deployment. Let's schedule a brief chat.
Best,
[Your Name]
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