Unsloth accused a brand new team (ByteShape) of "literally cheating." I brought the receipts, and Unsloth moved the goalposts.

Posted by TheRealSol4ra@reddit | LocalLLaMA | View on Reddit | 122 comments

TL;DR: I shared a highly efficient new model from a brand new team (ByteShape) in the Unsloth Discord. Instead of welcoming the competition or having a technical discussion, the Unsloth devs immediately accused them of "literally cheating" and manipulating data. When I reached out to ByteShape directly, they provided a highly professional, detailed technical breakdown, and even updated their graphs within 24 hours to appease Unsloth's complaints. Unsloth's response? They dismissed the technical facts, moved the goalposts to complain about the new graph, and shut down the conversation when they ran out of arguments.

Hey everyone. I’m making this post because I think the open source AI community thrives on innovation, collaboration, and healthy competition. Unsloth is undoubtedly one of the largest and most respected teams in this space right now, which is exactly why their recent conduct towards a brand new team needs to be called out.

As end users, we shouldn't have to deal with massive teams trying to bully newcomers out of the scene just because their numbers look threatening. Here is a chronological breakdown of exactly what happened over the last few days.

Part 1: The Spark

On April 10th, I posted in the Unsloth Discord about a newly released model from a team called ByteShape (Qwen3.5-35B-A3B). As an end-user, I was getting around 160 t/s, which was fantastic, and the size to performance ratio was incredibly impressive. I didn't post it as an attack, I was just sharing an interesting new development in the quantization space.

[My initial post introducing ByteShape's new model, sharing performance graphs, and noting the impressive generation speeds and size to performance ratio.](

Part 2: The Immediate Hostility and Accusations

Instead of taking it in stride, Unsloth developers Mike and Daniel immediately got defensive. Instead of discussing the tech, they went straight to accusations:

When I asked if they had actually contacted ByteShape about these massive accusations of data manipulation, Daniel scoffed at the idea, saying, "But why? They're a company," and pointed to a random Reddit comment he made as sufficient communication.

[Mike from Unsloth reacts defensively, accusing ByteShape of misrepresenting data with their graph labels and dismissing tokens per second (t\/s) as a \"silly metric.\"](

[Daniel jumps to conclusions, claiming the analysis is skewed and outright accusing ByteShape of \"literally cheating.\"](

[Daniel scoffs at the idea of formally contacting ByteShape, stating \"But why? They're a company\" and citing a random Reddit comment as sufficient communication.](

[Daniel doubles down on the baseless accusations, claiming ByteShape is cheating by using Quantization-Aware Training (QAT) on benchmark data.](

Part 3: Getting Real Answers

Since Unsloth refused to talk to the team they were accusing of fraud, I did. I sent an email to ByteShape detailing Unsloth’s specific complaints (the graph labels, the QAT cheating accusations, and dequantization overhead).

ByteShape responded immediately with incredible professionalism. They explained:

  1. No Cheating (PTQ, not QAT): They confirmed their approach is strictly Post Training Quantization. The dataset used for datatype allocation is fully isolated from benchmark datasets to prevent contamination.
  2. The Labels: They clarified the numbers were purely ordinal (ranking size within a specific family) and were never intended to be cross-method correspondences.
  3. Speed Explanations: They explained that because most LLM inference workloads are memory-bound, cutting data volume in half provides a massive speed benefit that heavily outweighs dequantization overhead, which is exactly why reporting end to end t/s matters.

They ended the email by thanking me for the questions, expressing excitement that people were enjoying the model, and asking for more feedback.

[ByteShape's highly professional email response clarifying their PTQ method, dataset isolation, and the reasoning behind their ordinal graph labels and speed metrics.](

Part 4: Moving the Goalposts

I brought this detailed, highly technical response back to the Unsloth Discord.

Mike completely ignored the technical clarifications about PTQ and memory bottlenecks. He hyper fixated entirely on the visual graph argument, stating that relying on hover-text is misleading for screenshots and concluding that "until they change their misleading graphs there's not much to say honestly."

Here is where it gets ridiculous. ByteShape actually listened. Within 24 hours, they updated their graphs, changing the numeric labels to letters (A, B, C) to completely eliminate any possibility of a "1 vs 1" misunderstanding.

When I showed Unsloth the updated graph, proving that ByteShape had actively accommodated their demands, Mike immediately complained that his demands were being framed as "trivial," and stated:

[Mike dismisses ByteShape's detailed technical explanations, hyper-fixating solely on the graph's visual labeling and arguing it is still misleading.](

[I reveal that ByteShape actually listened and updated their graph labels to letters (A, B, C) within 24 hours to accommodate Unsloth's specific complaints.](

[Moving the goalposts: Mike ignores ByteShape's visual fix, takes offense at his demands being called \"trivial,\" and invents a new rule that the exact quant names must be labeled.](

The Takeaway

The moment ByteShape fixed the graph, Unsloth invented a brand new rule to stay mad. It became painfully obvious that this was never about "misleading data" or protecting the consumer. It was about finding an excuse to invalidate a competitor.

When I finally laid out this entire timeline to them, pointing out how professionally ByteShape handled the situation compared to their own defensiveness, Mike's final response was to repeat the phrase "manipulating data" and end the conversation with:

If your models and methods are truly superior, you don't need to gatekeep visualization choices or accuse newcomers of cheating without proof. Unsloth has done incredible work for this community, but this behavior is entirely unacceptable for a team of their size and influence.

[The final exchange where Mike ignores the summary of events, repeats the \"manipulating data\" buzzword, and abruptly shuts down the conversation with \"Anyways moving on.\"](

We should be encouraging new teams like ByteShape who are bringing real innovation and speed to the table, not defending massive teams who throw a tantrum the second someone else posts a good benchmark.

Disclaimer: Please do not use this post as an excuse to harass the Unsloth team or send hate their way in their Discord or elsewhere. The goal of this post is simply to hold leaders in the community accountable, correct the record on a new developer's tech, and encourage better professional standards. Let's keep the discussion focused on the technology and fostering a welcoming open source environment.