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chipforge-llm
Ashx098/chipforge-llm
chipforge-llm is a machine learning model from Ashx098. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
LoRA adapters for Verilog HDL generation, fine-tuned on Nemotron-30B.
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Public
Updated Jul 15, 2026
Repo size
36.6 MB
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.safetensors54.9 MB · 99%
At a glance
- Access
- Public
- Created
- Jul 15, 2026
- Updated
- Jul 15, 2026
- SHA
- 1aad4ed3
Model card
From the Hugging Face model README
chipforge-llm
LoRA adapters for Verilog HDL generation, fine-tuned on Nemotron-30B.
phase_1/v0.1/
| Field | Value |
|---|---|
| Base model | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 |
| Training data | 36,321 Verilog modules (filtered ≤10k tokens) |
| Method | LoRA CPT (Continued Pre-Training) |
| LoRA rank | 8 |
| Trainable params | ~4.5M (0.015% of 30B) |
| Max seq len | 1024 |
| Epochs | 2 |
| Final train loss | 0.42 |
| Final eval loss | 0.43 |
| Token accuracy | ~89% |
| Hardware | 2x A100-80GB, FSDP |
Usage
``\python from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained( "NVIDIA/Nemotron-3-Nano-30B-A3B-BF16", torch_dtype="bfloat16", device_map="auto", trust_remote_code=True, ) model = PeftModel.from_pretrained(model, "Ashx098/chipforge-llm/phase_1/v0.1") tokenizer = AutoTokenizer.from_pretrained( "NVIDIA/Nemotron-3-Nano-30B-A3B-BF16", trust_remote_code=True, )
### wandb
Run: https://wandb.ai/avinash-mynampati-juspay/vaschpforge-llm/runs/c5gid36w
- Created
- Jul 15, 2026
- Updated
- Jul 15, 2026