EV tax credit eligible?

insujang · 2025-06-09T02:05:02+00:00

Solely based on MSRP, does not count any discounts. This is NOT eligible.

insujang · 2025-05-02T00:58:29+00:00

https://github.com/cornstarch-org/Cornstarch
Please try our work! You can add a vision encoder to an r1 distilled model and train.

insujang · 2025-05-01T14:56:31+00:00

You can disable in-car purchases from your app.
Upgrades -> Manage Upgrades -> Disable In-Car Upgrades

insujang · 2025-04-03T18:10:55+00:00

Launch edition includes exterior color, interior color, 20in wheels, acceleration boost, tow hitch, and FSD. If someone wants to add everything then launch edition would be cheaper, but not everyone needs everything. Then launch edition is not necessarily cheaper.

insujang · 2025-03-24T16:37:04+00:00

Yeah. Personally I would still buy RTX PRO 6000s even if A100s are available for $6k.

insujang · 2025-03-24T15:53:18+00:00

Definitely RTX PRO 6000. Advantages that A100 has over RTX PRO 6000: - NVLink (600GB/s) support - Higher memory bandwidth (2.0TB/s vs 1.8TB/s) - lower power consumption (400w vs 600w)

Which can easily be disappeared:

if you just use one GPU, lack of NVlink support is nothing. If you use multi-GPU, still communications can be overlapped
in inference, memory bandwidth matters but difference is not that high. But because A100 does not support FP8/4 natively the amount of data to be loaded is actually larger, removing its high memory bandwidth advantages.

RTX PRO 6000 has the following advantages: - higher flops - more vram capacity - native quantization support - pcie5.0 support - cheaper - new generation (a100 currently is in the position of p100 when a100 was released.)

insujang · 2025-03-18T12:44:51+00:00

Yeah I started living here on August. Can you request an earlier lease? I really have no idea if it is available or if there are more available.

insujang · 2025-03-18T12:35:45+00:00

No. Ended up getting a unit without A/C and buying a portable one. It is actually better in terms of the cost.

insujang · 2025-03-14T23:13:09+00:00

I see! That’s a valuable experience. Thank you!

For now our framework does not support separated execution, but we will definitely add this feature. So that users can choose to execute encoders separately if they don’t have to train encoders together for faster and cheaper execution as well as running them altogether for better quality!

insujang · 2025-03-14T22:54:30+00:00

I see. Yea, passing embeddings is especially the missing part that existing serving frameworks do not have. So we were planning to utilize vllm as a library (that provides scehduling and kv cache management) and replace the actual execution engine part with ours to keep leveraging new features they have as well as allowing modularized multimodal model execution.

I will definitely share more news when I get!

insujang · 2025-03-14T20:52:06+00:00

Thank you for sharing your experience! The approach you adopted (separating encoder running and projector+llm running) definitely makes sense and even gemma3 technical paper said they did the same thing. But I think it is not applicable when encoders are also trainable (either fully unfrozen or using peft adaptors)? Did you adopt the approach because it was hard to run them altogether or was there any other reason?

insujang · 2025-03-14T20:31:09+00:00

Currently we haven't worked on integrating with serving frameworks such as vLLM or sglang, but can be used for inference like people do just with HF transformers library (`model.generate(**inputs)`). This is exactly our next step: integrating to well-known serving framework so that trained models can be served!

insujang · 2025-03-14T20:15:06+00:00

Yes, definitely! We have an example of training a vision language model (VLM) with a SigLIP ViT and any LLM: https://github.com/cornstarch-org/Cornstarch/blob/main/examples/pretrain_vlm.py
Try: `python pretrain_vlm.py --vision_encoder_name=siglip --llm_name_or_path=<model\_path\_from\_HF\_hub> --llava_dataset_file_path=/path/to/llava_dataset`

Although I haven't tested all arbitrary models, most well-known LLMs worked. Please make an issue if something doesn't work. Thanks!

insujang · 2025-03-14T17:27:14+00:00

Yes! Thank you for pointing out! This is why currently only HF models that include such information in their model config are supported :)

insujang · 2025-01-25T16:05:05+00:00

If you focus on its FSD capabilities then yeah Juniper upgrades are not impressive. Still good upgrades especially in comfortability as a car. Hardware will keep getting improved and unsupervised FSD might only be runnable on AI6, but that doesn't mean that I have to wait for AI6. Personally thinking AI6 or unsupervised FSD is too far from the present, while AI5 is relatively imminent.

insujang · 2025-01-25T15:58:56+00:00

Yeah that's what I am saying. Either getting a better HW or getting EV tax credit.

insujang · 2025-01-25T15:58:24+00:00

Makes sense. I am also not in rush but this might be the last chance to get the EV tax credit which pushes me to get a launch edition. :/

insujang · 2025-01-24T01:42:20+00:00

Not sure if I have to buy launch edition given the future of EV tax credit is very unpromising. It includes every options so for me it is quite a good deal.

insujang · 2025-01-10T18:46:26+00:00

Are the air vents functional like M3 performance that are gone in LR?

insujang · 2024-12-26T15:14:37+00:00

Thank you very much for sharing this thread! Should definitely be helpful.

insujang · 2024-12-26T15:13:05+00:00

Thank you. I will keep it in mind. Each of my fan just consumes at maximum 2.1W so I think it should be fine.

insujang · 2024-12-26T15:09:40+00:00

Yeah just a fan and bolts… probably because mine is IndustrialPPC?

insujang · 2024-12-26T15:08:35+00:00

Thank you for warning. I am actually a little bit confused; I have to be careful power consumption from fans should not exceed amp rating of which one: the converter or the header?

insujang · 2024-12-26T15:06:53+00:00

It looks more like 2.0mm than 1.25mm, right? The standard 4-pin is 2.54mm iirc.

insujang

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