The Qwen 35B Debacle: Weighing LocalLLaMA Options

The recent commits that removed the Qwen 35B model from LocalLLaMA have left some users scrambling for alternatives. As u/LLaMA_user123 pointed out, “the Qwen 35B was a significant part of what made LocalLLaMA so attractive in the first place.” I tend to agree - this is overkill for most people, who just want a reliable, plug-and-play LLaMA experience.

The Fallout #

The Qwen 35B’s removal has sparked a heated discussion, with some users threatening to switch to alternative LLaMA implementations like LLaMA-7B or even proprietary options like Meta’s LLaMA. I love this tool, but it has one fatal flaw: its resource requirements. Running a Qwen 35B model requires at least 16GB of RAM, which can be a significant hurdle for users with lower-end hardware. On the other hand, the 7B model can run on as little as 4GB of RAM, making it a more accessible option for those with limited resources.

One comment that caught my eye was from u/TechnoTim, who suggested using Docker to containerize LocalLLaMA and mitigate some of the resource issues. While this is a viable solution, I haven’t tested this on ARM, so your mileage may vary. That being said, if you’re running LocalLLaMA on a cloud provider like Hetzner or DigitalOcean, you can easily spin up a instance with plenty of RAM to spare - I’ve seen prices as low as $5/month for a 16GB instance.

Alternative Solutions #

So, what are the alternatives to the Qwen 35B? One option is to use the LLaMA-7B model, which has a significantly lower RAM requirement. However, this comes at the cost of performance - the 7B model is roughly 30% slower than the Qwen 35B. Another option is to use a different LLaMA implementation altogether, like the one provided by the LLaMA-13B project. This implementation has a similar performance profile to the Qwen 35B, but requires even more RAM - a whopping 32GB.

In terms of setup time, I’ve found that LocalLLaMA is generally quicker to get up and running than the alternative implementations. With LocalLLaMA, you can have a working LLaMA instance in under 10 minutes, whereas the other implementations can take upwards of 30 minutes to an hour to set up. However, this is largely dependent on your familiarity with the technology - if you’re comfortable with Docker and containerization, the alternative implementations may be just as easy to set up.

Community Response #

The community is genuinely split on this issue, with some users passionately defending the Qwen 35B and others advocating for the 7B model. As u/LLaMA_newb pointed out, “the 7B model is still a great option for most use cases, and it’s way more accessible than the Qwen 35B.” I tend to agree - while the Qwen 35B may have been a nice-to-have, it’s not essential for most users.

In terms of benchmarks, I’ve seen some impressive results from the LLaMA-13B project, with performance gains of up to 20% over the Qwen 35B. However, this comes at the cost of increased RAM usage, which may be a deal-breaker for some users. Ultimately, the choice of LLaMA implementation will depend on your specific use case and requirements - if you need raw performance, the LLaMA-13B may be the way to go, but if you’re looking for a more accessible option, the 7B model may be a better fit.

FAQ #

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