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ostlerai/vance-4b-instruct

AI model by ostlerai

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Model card & documentation

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VANCE is a specialized 4-billion parameter open-weight model designed to assist ASIC and FPGA developers in generating high-quality SystemVerilog RTL from natural-language specifications. Based on the Qwen3-4B-Base architecture, VANCE is engineered to provide GPT-4o-level performance while remaining small enough to run locally on a standard engineering laptop, ensuring data privacy and eliminating API costs. VANCE is a fine-tuned version of Qwen3-4B-Base. VANCE was developed through a rigorous two-stage training pipeline to ensure both syntactic correctness and functional accuracy. To avoid "LLM-collapse" and ensure high-quality organic code, the training set consists of:…

ostlerai/vance-4b-instruct on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. VANCE (Verilog Assistant Novel Chatbot Entity)
  2. Highlights
  3. Model Overview
  4. Training Methodology
  5. 1. Data Curation
  6. 2. Training Pipeline
  7. Performance
  8. Quickstart
  9. Best Practices for RTL Generation
  10. Citation

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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

ostlerai/vance-4b-instruct is a task-unspecified model repository published by ostlerai on Hugging Face. A library was not reported.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
Not reported
Library
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Recent downloads (30 days)
0
Cumulative likes
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Hugging Face trending score
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Reported safetensors parameters
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Architecture
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License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-17T06:32:28.000Z
Last modified
2026-10-17T06:37:54.000Z

Compatibility and lineage

Reported languages: en

Reported base models: Qwen/Qwen3-4B-Base

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-17T06:42:57.152Z. Daily imports are snapshots, not real-time monitoring.

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