Freesolo Flash

Full-Stack Platform for Training Small Language Models

148 followers

Full-Stack Platform for Training Small Language Models

148 followers

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AI Infrastructure Tools

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LLM Fine Tuning

Freesolo helps enterprise teams turn generic model capability into AI features that belong in the product. We make reinforcement learning a commodity so any team can train a small, specialized model for their task.

Free

Launch tags:SaaS•Artificial Intelligence

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ReplayAutonomous app testing for teams who ship at AI-speed

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Clado

Maker

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Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. However, engineers historically had to choose between model size and quality; as models got smaller, performance, adherence, and recall dropped linearly. Post-training on production data closes that gap. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent. We accomplish this in a few different ways: - Upfront pricing: instead of billing by GPU hours or tokens spent while training, we quote the cost of the entire run upfront, so your agent can accurately tweak the dataset, model size, and algorithms it uses while staying in your budget before it starts the run. - Our GPU infrastructure is optimized to make your specific run as in-expensive and fast as possible. This optimization means training with flash is 8x less expensive for SFT and 5.5x less expensive for GRPO (RL) when compared to Tinker. - Environment hub: Our custom environment SDK allows you to build environments in a modular way and perfectly integrates with our asynchronous training framework. Flash is built out of our own frustrations with current managed post-training solutions, especially for SLMs. We believe that unlocking frontier capability for a narrow task into a small model will prove to be the best improvement for all agentic product ux. Flash is our first step towards solving this. Just grab a Freesolo API Key, point your agent at the training package, and watch it push your lightweight model beyond the frontier.

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2mo ago

Hey. Upfront pricing quoted before the run starts is the part I'd stress test, since that only works if the cost estimate is actually accurate to what the training ends up needing. RL runs in particular are notoriously unpredictable in how many steps or rollouts it takes to converge, especially on a narrow task where the reward signal might be noisy early on. If a GRPO run needs meaningfully more steps than estimated to actually reach a usable policy, does Freesolo eat that overage to honor the quoted price, or does the agent get cut off at the budget with a model that never really finished training. Also curious how the environment hub interacts with that pricing model. If someone builds a custom environment through your SDK that behaves in some unexpected way, say a reward function that's easy to game or a slow environment step, does that variability get priced into the upfront quote too, or is upfront pricing really only reliable for the standard environments you already understand well.

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2mo ago

Dial

the 8x and 5.5x cheaper than Tinker numbers are the part I'd want to see backed up before taking at face value - what's the actual comparison methodology there? same model size, same dataset, same convergence criteria on both sides, or is it comparing your optimized infra against their default settings without controlling for what counts as a finished run on each platform

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1mo ago

Full-stack training platforms for SLMs feel like the right direction as more people realize they don't need a massive model for most tasks. Does it handle the data prep/cleaning side too, or is that still on the user before they get to training?

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2mo ago

Interesting. Most small teams can't even think about custom models because RL infra is too expensive. What level of ML knowledge is needed — can a full-stack dev with no ML background train something useful?

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1mo ago

The upfront cost quote before committing to a run is the detail that actually changes how you use this — your coding agent can tune dataset size and algorithm choice on price, not guesswork. Curious about the environment SDK: when you build a custom environment and upload training data, does the corpus stay in your own infra or is it transferred to Freesolo servers for the actual GPU run? That data-boundary question matters a lot for anything with proprietary or sensitive training sets.

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1mo ago

Dial

upfront pricing for training runs is the detail that stands out, most of the pain with RL post-training is the unpredictable GPU bill once a run goes longer than expected. the 8x/5.5x numbers against Tinker are a strong claim - is that on a specific task/model size you benchmarked, or does the cost advantage hold pretty evenly across the range of models people train on Flash?

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1mo ago

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