Hey HN family! I found a few bugs for Phi-4 - Microsoft's latest MIT licensed LLM to be on par with GPT-4o mini
1. End of sentence should be <|im_end|> not <|endoftext|>
2. Chat template should not auto add an assistant prompt
3. Padding token should not be EOS but <|dummy_87|>
I also converted Phi-4 to Llama-arch. I uploaded GGUFs, 4bit quants, dynamic quants and all fixes to https://huggingface.co/unsloth
I also made a Colab notebook to finetune Phi-4 on a free GPU: https://colab.research.google.com/github/unslothai/notebooks...
> We converted Phi-4 to Llama’s architecture for better accuracy and easier use.
What does this mean? When I think about "model architecture", I think about the number of weights in each layer, the organization of the layers, etc. And AFAIK, it's untenable to "port" a model from one to the other without effectively retraining it. So what does it actually mean to "convert to Llama's architecture"?
Oh Phi-4's architecture is inspired from Llama itself, except they merged the attention matrices into 1 large matrix for better FLOP utilization, and the gate/up matrices in the MLP.
Phi-3 use to use sliding window attention, but they got rid of that in Phi-4.
So, you can "Mistral-fy" Phi-3 and convert it to Mistral arch (by unmerging the merges), and now you can "Llama-fy" Phi-4 to Llama arch.
The reason why accuracy increases in finetuning is because during LoRA finetuning, you learn only 1 A matrix for merged QKV, whilst unmerging it creates 3 A matrices - this allows the model to have more freedom to learn new features.
Would guess GGUF so you can run on llama.cpp, LM Studio, etc..., but OP can hopefully clarity further for you.
Yep converting to Llama arch definitely makes accessibility much better - also many fast LLM serving libraries normally support Llama, so it makes it easier to port and use!
Wasn't Phi-3 also bugged/is still bugged? Seems like Microsoft just doesn't care.
>to be on par with GPT-4o mini
Phi is known to overfit benchmarks. It's way, way worse then that.
Phi-3 should be fixed as well - but yes there were bugs as well! https://x.com/danielhanchen/status/1782853167572832650
Phi-3's sliding window should be 2048 and not 2047, and they also had chat template issues - I uploaded correct versions to https://huggingface.co/unsloth/Phi-3.5-mini-instruct
Anecdotally, I've been experimenting with Phi-4 the past hour or so (so, yeah, not very comprehensive) and it's certainly a strong model. Definitely better than the previous Phi models.
Yep Phi-4 definitely is better than Phi-3.5!
Huh! That may explain why I kept on getting visible <|im_end|> output when I tried running a Phi-4 GGUF file using llama.cpp.
Oh yes exactly! I trimmed it out now :)
The better chat template should be:
{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}
Can you convert to ONNX so I can try in web browser?
Would like to update this:
https://huggingface.co/spaces/webml-community/phi-3.5-webgpu
Oh I can probs try doing this!
Update: The Phi-4 team is actively working on adding all our fixes into the original model! https://huggingface.co/microsoft/phi-4/discussions/21
Daniel’s fixes to Phi-4 make it the best scoring Phi-4 on HF’s Open LLM Leaderboard. Great job on that.
Unsloth is a masterpiece, keep up the great work!
Thanks a lot!
Application Error
TypeError: m(...).findLast is not a function
at L (https://unsloth.ai/assets/root-DexjOeLv.js:1:340)
at ia (https://unsloth.ai/assets/components-D38fXVcE.js:7:30549)
at Ac (https://unsloth.ai/assets/components-D38fXVcE.js:7:98661)
at Am (https://unsloth.ai/assets/components-D38fXVcE.js:7:94250)
at o0 (https://unsloth.ai/assets/components-D38fXVcE.js:7:93401)
at ha (https://unsloth.ai/assets/components-D38fXVcE.js:7:93212)
at Mm (https://unsloth.ai/assets/components-D38fXVcE.js:7:90555)
at Om (https://unsloth.ai/assets/components-D38fXVcE.js:7:89963)
at MessagePort.M (https://unsloth.ai/assets/components-D38fXVcE.js:1:11235
Sorry are there some issues with our website?
yep, it appears for a second - then displays only this :(
Oh no :( Do you know which device / platform?
Available on Ollama already: https://ollama.com/vanilj/phi-4-unsloth
looking at "original" Phi4 on ollama, it looks like they have fixed parameters issue for im_start/end
Oh fabulous! :)
The benchmark results of the model before and after the "fixes" do not match numbers reported in the model card: https://huggingface.co/microsoft/phi-4
According to Microsoft MATH score should be 80.4, while both original and the "fixed" models as run by unsloth only score just over 12.3. So either Microsoft made a few huge mistakes, or unsloth was not able to run their model correctly.
Oh yes I found this to be a bit strange - I uploaded our versions and Microsoft's own version to Hugging Face's public LLM leaderboard - https://huggingface.co/spaces/open-llm-leaderboard/open_llm_...
You can see Microsoft's own original Phi-3 scores 12.31% - I'm unsure why. My fixes at least pushes it to 20%.
It's possible because HF's benchmark does "Scoring: Exact match: Was the solution generated correct and in the expected format" which might be the issue
Are there alternatives to unsloth?
I would love to use it but the open/free version only handles one GPU, and it's unclear how much the paid version would cost. I have some limited access to multiple older NVidia cards and would love to make better use of them while I'm still learning. My budget for learning/projects is rather modest.
Hopefully they succeed. At work I could make a strong case for going with them as they allow keeping data local only, instead of relying on an API.
Multi GPU support is definitely coming to Unsloth OSS! Our goal was to release it this month, but unsure on exact timelines - maybe next month!!
Thank you!
I'll ping you when it comes along!
"Yes it improves performance!" proceeds to show the most unconvincing stats ever
you can probably blow on your GPU and get a similar performance change
I uploaded our fixed versions to https://huggingface.co/spaces/open-llm-leaderboard/open_llm_... which show the difference in scores.
I agree it's not super convincing, so I provided anecdotal evidence as well - I'll work with the Phi-4 team to upstream these fixes!
PS for further credibility, we also fixed 8 bugs in Gemma 1 - see https://x.com/danielhanchen/status/1765446273661075609 , multiple bugs in Llama, Mistral, Qwen and other models
I'm sorry, I don't understand what you mean. I checked the original article again too. As it stands, my understanding is you are claiming:
- blowing on a GPU (which I take to mean doing roughly nothing)
- gets roughly the same perf change
- as moving from fp16 to q4
Are you referring to the finetuning part?
The multiple bug fixes are separate from the finetuning sections - Unsloth itself makes finetuning 2x faster and use 70% less memory - the bug fixes are totally detached from finetuning - ie you can take the fixed version we uploaded at https://huggingface.co/unsloth/phi-4, and use it in any framework or inference engine.
Apologies I'm confused on the comment sorry.
If you're questioning the credibility of the bug fixes - we fixed 8 bugs in Gemma https://x.com/danielhanchen/status/1765446273661075609, multiple bugs in Llama, Mistral, Qwen, a gradient accumulation bug https://x.com/danielhanchen/status/1846235913443262891 and much more
2x faster than what?
Oh 2x faster and uses >70% less memory than Hugging Face + Flash Attention 2! I did a CUDA / GPU Mode talk about it here: https://www.youtube.com/watch?v=hfb_AIhDYnA Also to the PyTorch team here: https://www.youtube.com/watch?v=MQwryfkydc0 and the PyTorch Conference here: https://www.youtube.com/watch?v=PdtKkc5jB4g
Update - the Phi-4 team is working on adding all our fixes to the original model! https://huggingface.co/microsoft/phi-4/discussions/21
Ah yes, drawing ASCII art, the de facto benchmark for evaluating LLM quality.
Anecdotal evidence was provided to show some Redditors tested it out - but I do agree it's not correct to show that as an example - so I uploaded our fixed versions to Hugging Face's public LLM leaderboard here: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_... - this shows the fixes do in fact work!
>Reddit comments show our fixes make Phi-4 inference much better
I’d like to try ‘Reddit comments show my fixes make app better’ in my next review
Fixed versions are also independently scored by Hugging Face's Open LLM Leaderboard: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_...
The Reddit LocalLlama community is actually pretty cool - tonnes of research actually comes from the community - for example kaiokendev's linear RoPE scaling, YaRN, NTK Aware RoPE Scaling, many LLM benchmarks - many researchers use LocalLlama to share research and discuss on new stuff.
I know a lot of AI researchers use the "LocalLlama vibe check" which essentially is an anecdotal approach to LLM evaluation - ie instead of relying on Chat LMsys or LLM benchmarks, 3rd party crowd sourced vibe checks sometimes do much better.
As an update - the Phi-4 team is actively working on incorporating all fixes! See https://huggingface.co/microsoft/phi-4/discussions/21