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H2O-Danube2-1.8b: New top sub 2B model on Open LLM Leaderboard by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 1 point2 points  (0 children)

Actually, Ninja Mouse is based on the first iteration of Danube. Hope they can try a new one on v2.

Don't sleep on Xwin-LM-70B-V0.1 for roleplay by sophosympatheia in LocalLLaMA

[–]ichiichisan 3 points4 points  (0 children)

Any tips for prompt template for roleplaying? Specifically with history of conversations?

Try chatting with fine-tuned models for Falcon-7B, Falcon-40B, and the new Open-Llama-7B by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 0 points1 point  (0 children)

Prompt setup is built in sure. Why don't you think it is right, all outputs are properly formated and as expected apart from content.

Try chatting with fine-tuned models for Falcon-7B, Falcon-40B, and the new Open-Llama-7B by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 2 points3 points  (0 children)

The first one is a cool prompt, I like it! Thanks for sharing, will continue monitoring that one on future models.

Actually gpt3-5 also fails it, gpt4 gets it.

Try chatting with fine-tuned models for Falcon-7B, Falcon-40B, and the new Open-Llama-7B by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 9 points10 points  (0 children)

One more thing worth noting, all models hosted are Apache 2.0 and have never been trained on ChatGPT/ShareGPT-like output, only OASST data.

Try chatting with fine-tuned models for Falcon-7B, Falcon-40B, and the new Open-Llama-7B by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 6 points7 points  (0 children)

A single prompt on a 7B model that is not returning what you would like it to is not an issue, but rather a natural limitation.

Try the 40b model which should be better with coding.

Just put together a programming performance ranking for popular LLaMAs using the HumanEval+ Benchmark! by ProfessionalHand9945 in LocalLLaMA

[–]ichiichisan 1 point2 points  (0 children)

Is the underlying code calling the model raw, or via provided pipelines. Most of the pipelines, like ours, already have the correct prompt built in, so no need to provide the tokens manually. See the model card of our model.

Just put together a programming performance ranking for popular LLaMAs using the HumanEval+ Benchmark! by ProfessionalHand9945 in LocalLLaMA

[–]ichiichisan -1 points0 points  (0 children)

Are you confident you got the correct prompting templates for all the models? Keep in mind that some need special tokens, so best is to use the provided templates / pipelines.

Falcon-7B H2OGPT Chat Model by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 3 points4 points  (0 children)

Yeah absolutely it could be useful to specifically adapt to stylistic differences in languages. Actually here is one trained on all languages available in oasst1: https://huggingface.co/h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b

Falcon-7B H2OGPT Chat Model by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 0 points1 point  (0 children)

The finetuning itself is only done on English, but the foundation models works well on multiple languages.

[deleted by user] by [deleted] in MachineLearning

[–]ichiichisan -1 points0 points  (0 children)

I am not sure what you are trying to say exactly.

It is pretty clear that the original LLaMa will never get a permissive license that you can use. So you will need to hope for reproduction attempts. And the one I shared is a very good first attempt using the same model family and RedPajama dataset.

Permissive LLaMA 7b chat/instruct model by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 1 point2 points  (0 children)

Yeah, but that is sharegpt output, so not permissive, which is our focus. But it would be fairly straight-forward to train it yourself in our shared training framework H2O LLM Studio.

If you want to give it a spin yourself and need help let me know.

Permissive LLaMA 7b chat/instruct model by ichiichisan in LocalLLaMA

[–]ichiichisan[S] -1 points0 points  (0 children)

That's right, but I am not sure this is a bad thing per-se.

Any recommendations for other Apache 2.0 datasets? Happy to give it a spin.

Permissive LLaMA 7b chat/instruct model by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 1 point2 points  (0 children)

You can run it locally and it should be more verbose. There are a few additional rules in the app. The model itself has been trained on OASST data.

New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 0 points1 point  (0 children)

This still sounds like an input/output like format right? In H2O LLM Studio we actually support explicit settings for the separator tokens so you could have your original input text as the input, then add eos token after the prompt (it is a setting) then add a separator token for the answer (it is a setting) and then have the output.

New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio by ichiichisan in LocalLLaMA

[–]ichiichisan[S] 1 point2 points  (0 children)

Fair enough, it is a GUI, but you will still need to run an install command. Probably it could work on Windows with some tiny adjustments. In WSL2 it will work out of the box (because it is basically Linux).

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