3 ms·

The speed of the chatbot's response is startling when you're used to the simulated fast typing of ChatGPT and others. But the Llama 3.1 8B model Taalas uses pre

The speed of the chatbot's response is startling when you're used to the simulated fast typing of ChatGPT and others. But the Llama 3.1 8B model Taalas uses predictably results in incorrect answers, hallucinations, poor reliability as a chatbot. What type of latency-sensitive applications are appropriate for a small-model, high-throughput solution like this? I presume this type of specialization is necessary for robotics, drones, or industrial automation. What else?

app13 8mo ago
Routing in agent pipelines is another use. "Does user prompt A make sense with document type A?" If yes, continue, if no, escalate. That sort of thing
- mtone 8mo ago
  For this type of repetitive application I think it's common to "fine-tune" a model trained on your business problem to reach higher quality/reliability metrics. That might not be possible with this chip.
  - mike_hearn 8mo ago
    They say LoRA finetunes work.
freeone3000 8mo ago
Maybe summarization? I’d still worry about accuracy but smaller models do quite well.
freakynit 8mo ago
You could build realtime API routing and orchestration systems that rely on high quality language understanding but need near-instant responses. Examples: 1. Intent based API gateways: convert natural language queries into structured API calls in real time (eg., "cancel my last order and refund it to the original payment method" -> authentication, order lookup, cancellation, refund API chain). 2. Of course, realtime voice chat.. kinda like you see in movies. 3. Security and fraud triage systems: parse logs without hardcoded regexes and issue alerts and full user reports in real time and decide which automated workflows to trigger. 4. Highly interactive what-if scenarios powered by natural language queries. This effectively gives you database level speeds on top of natural language understanding.
zardo 8mo ago
I'm wondering how much the output quality of a small model could be boosted by taking multiple goes at it. Generate 20 answers and feed them back through with a "rank these responses" prompt. Or doing something like MCTS.
- freakynit 8mo ago
  Isn't this what thinking models do internally? Chain of thoughts?
  - andy12_ 8mo ago
    No. Chain of thought it just the model generating a single answer for longer inside <think></think> tags which are not shown in the final response. The strategy of generating different answers in parallel is something different (which can be used in conjunction with chain of thought) and is the thing used by models like Gemini 3 Deep Think and GPT-5.2 Pro.
    - freakynit 8mo ago
      Hmm.. got it. Thanks..
energy123 8mo ago
Coding, for some future definition of "small-model" that expands to include today's frontier models. What I commented a few days ago on codex-spark release: """ We're going to see a further bifurcation in inference use-cases in the next 12 months. I'm expecting this distinction to become prominent: (A) Massively parallel (optimize for token/$) (B) Serial low latency (optimize for token/s). Users will switch between A and B depending on need. Examples of (A): - "Use subagents to search this 1M line codebase for DRY violations subject to $spec." An example of (B): - "Diagnose this one specific bug." - "Apply these text edits". (B) is used in funnels to unblock (A). """
scotty79 8mo ago
Language translation, chunk by chunk.

app13 8mo ago

Routing in agent pipelines is another use. "Does user prompt A make sense with document type A?" If yes, continue, if no, escalate. That sort of thing

mtone 8mo ago
For this type of repetitive application I think it's common to "fine-tune" a model trained on your business problem to reach higher quality/reliability metrics. That might not be possible with this chip.
- mike_hearn 8mo ago
  They say LoRA finetunes work.

freeone3000 8mo ago

Maybe summarization? I’d still worry about accuracy but smaller models do quite well.

freakynit 8mo ago

You could build realtime API routing and orchestration systems that rely on high quality language understanding but need near-instant responses. Examples: 1. Intent based API gateways: convert natural language queries into structured API calls in real time (eg., "cancel my last order and refund it to the original payment method" -> authentication, order lookup, cancellation, refund API chain). 2. Of course, realtime voice chat.. kinda like you see in movies. 3. Security and fraud triage systems: parse logs without hardcoded regexes and issue alerts and full user reports in real time and decide which automated workflows to trigger. 4. Highly interactive what-if scenarios powered by natural language queries. This effectively gives you database level speeds on top of natural language understanding.

zardo 8mo ago

I'm wondering how much the output quality of a small model could be boosted by taking multiple goes at it. Generate 20 answers and feed them back through with a "rank these responses" prompt. Or doing something like MCTS.

freakynit 8mo ago
Isn't this what thinking models do internally? Chain of thoughts?
- andy12_ 8mo ago
  No. Chain of thought it just the model generating a single answer for longer inside <think></think> tags which are not shown in the final response. The strategy of generating different answers in parallel is something different (which can be used in conjunction with chain of thought) and is the thing used by models like Gemini 3 Deep Think and GPT-5.2 Pro.
  - freakynit 8mo ago
    Hmm.. got it. Thanks..

energy123 8mo ago

Coding, for some future definition of "small-model" that expands to include today's frontier models. What I commented a few days ago on codex-spark release: """ We're going to see a further bifurcation in inference use-cases in the next 12 months. I'm expecting this distinction to become prominent: (A) Massively parallel (optimize for token/$) (B) Serial low latency (optimize for token/s). Users will switch between A and B depending on need. Examples of (A): - "Use subagents to search this 1M line codebase for DRY violations subject to $spec." An example of (B): - "Diagnose this one specific bug." - "Apply these text edits". (B) is used in funnels to unblock (A). """

scotty79 8mo ago

Language translation, chunk by chunk.