DeepSeek-R1-Distill-Qwen-7B Garbled Text Issue On N300D

by Kenji Nakamura 56 views

Hey guys! Let's dive into a peculiar issue we've encountered with the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model on a single N300D card using tt-metal. It's a bit of a head-scratcher, so let’s break it down.

The Bug: Garbled Text Galore

So, what's happening is that when we run the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model, it starts off all smooth, streaming readable text like a champ. But then, out of nowhere, the response just nose-dives into a massive block of garbled characters. Think of it as a digital version of alphabet soup gone wrong – mojibake and corruption taking over! We tried tweaking the generation parameters, hoping that might do the trick, but no luck. The weird part? Other models like deepseek-ai/DeepSeek-R1-Distill-Llama-8B and Qwen/Qwen2.5-7B stream perfectly fine in the same setup. It’s like this particular Qwen-distill model is throwing a tantrum.

When you're working with large language models, the clarity and integrity of the output are paramount. The garbled text issue with the DeepSeek-R1-Distill-Qwen-7B model directly impacts usability, making it impossible to rely on the model for tasks requiring accurate text generation. This issue highlights the importance of thorough testing and validation of models across different hardware configurations. The inconsistent behavior between the Qwen-distill model and other models like DeepSeek-R1-Distill-Llama-8B and Qwen2.5-7B suggests a potential incompatibility or a bug specific to the interaction between the model and the N300D card via tt-metal. It's not just about seeing the words; it’s about understanding them. Imagine asking for a summary of a complex topic and getting back a jumbled mess. That’s why this issue needs some serious attention.

We've included some screenshots to give you a visual of what we're seeing. If you need the raw streamed chunks or logs, just give us a shout, and we’ll get them over to you. From our internal discussions, it seems this specific Qwen-distill model hasn’t gone through the same rigorous customer-readiness testing as the DeepSeek Llama distill models. So, we’re flagging it here for a deeper dive and investigation. It’s crucial to ensure that all models meet a certain standard of performance before they’re widely used. The reliability of these models is key to their adoption and effectiveness in real-world applications. Think about it – if you can’t trust the output, you can’t trust the model. And that’s a deal-breaker.

Models in the Hot Seat

  • Problem Child: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B (the one acting up)
  • Good Guys: deepseek-ai/DeepSeek-R1-Distill-Llama-8B and Qwen/Qwen2.5-7B (streaming like champs)

Steps to Reproduce: Let's Get Technical

Alright, if you're up for recreating this funky behavior, here’s the lowdown on how to do it:

  1. Load it Up: Get that deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model onto a single N300D using tt-metal. Think of it as setting the stage for our little drama.
  2. Start the Show: Kick off a streaming chat session with any ol' prompt. Something straightforward, like “List all prime numbers less than 100,” should do the trick. We’re not trying to stump the model here, just get it talking.
  3. Observe the Chaos: Here’s where the fun begins. Watch as the initial part of the reply comes out nice and clear. But then, dun dun DUN, the stream spirals into a block of garbled characters until the generation wraps up. It’s like watching a perfectly good sentence fall apart in real-time.
  4. Repeat for Sanity: Now, do the same thing with deepseek-ai/DeepSeek-R1-Distill-Llama-8B and Qwen/Qwen2.5-7B. You should see them stream along without any corruption. This step is crucial to confirm that the issue is specific to the Qwen-distill model and not some broader problem with your setup.

The ability to reproduce an issue is a cornerstone of debugging. By providing clear, step-by-step instructions, we make it easier for others to verify the bug and contribute to its resolution. This collaborative approach is essential in the open-source community. Furthermore, the contrasting behavior between different models under the same conditions pinpoints the problem more accurately. It narrows down the potential causes and helps focus the investigation on the specific interactions between the DeepSeek-R1-Distill-Qwen-7B model and the tt-metal environment. Think of it as detective work – each clue helps us get closer to the culprit.

Expected Behavior: What We Want

In a perfect world, here’s what we’d expect to see:

  • Crystal Clear Text: Fully readable UTF-8 text flowing throughout the entire stream. No weird symbols, no gibberish, just plain old understandable language.
  • Proper Endings: Smooth EOS/stop handling. The model should know when to stop and not just trail off into the abyss.
  • No Corruption Zone: Zero garbled or corrupted output. We want clean, consistent text from start to finish.

Expectations are the foundation of quality assurance. When we clearly define what we expect, we set a benchmark against which we can measure performance. In the context of language models, the expectation of fully readable UTF-8 text is non-negotiable. The models are designed to generate human-readable content, and any deviation from this expectation is a clear sign of a problem. Similarly, proper EOS/stop handling is crucial for a seamless user experience. A model that doesn't know when to stop can produce incoherent and irrelevant output. The absence of garbled output is simply a matter of data integrity. We need to ensure that the information generated by the model is not corrupted or distorted in any way. These expectations collectively define the baseline for a reliable and usable language model.

Environment Information: The Scene of the Crime

Crickets chirping... No response provided.

Conclusion: Time to Investigate

So, there you have it. The deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model is giving us some headaches with its garbled text shenanigans on the N300D. The good news is we’ve isolated the issue, laid out the steps to reproduce it, and know what we expect to see instead. Now, it’s time for the real detective work to begin. Let’s get this bug squashed!

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  • DeepSeek-R1-Distill-Qwen-7B
  • N300D card
  • tt-metal
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  • DeepSeek-R1-Distill-Llama-8B
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