LLMScalelive Hugging Face data

Task fit — is this LLM right for the job?

Is this model built for the job? Pick a model and your use-case — LLMScale matches the characteristics the model declares (size, context, tuning, modality) against what the task needs, and shows every criterion it used.

1

Choose a model

built-in DBqwen232.76B params·64L · 5120d · 40/8 headsGQA
2

Choose your task

Weighs instruction tuning most, then size (7B or more) and an 8k context.Weighs code training most, then instruction tuning, size (7B or more) and a 16k context.Weighs context length most (32k or more), then instruction tuning and size.Weighs context length most (64k or more), then instruction tuning and size.Weighs instruction tuning and reasoning, then size (14B or more) and a 16k context.Weighs reasoning training most, then size (14B or more) and a 16k context.Weighs multilingual training most, then instruction tuning and size.Needs image input; then weighs instruction tuning and size.Needs an embedding model; a small one scores well.Weighs a small model most (3B or less is best), then instruction tuning.Weighs fast decoding most, favoring a mixture of experts or a small dense model; then instruction tuning and an 8k context.

Capability match

100/100
of the criteria checked
Built for this

Read from what the model declares — size, context, tuning, modality. It cannot see answer quality, grounding or hallucination rate, so a high match means “built for this”, not “good at this”. Only your own evaluation settles that.

Why this score
  • Model type15%

    Generative text model

  • Instruction tuning45%

    Chat / instruction-tuned

  • Model size25%

    33B (recommended ≥ 7B)

  • Context window15%

    32k tokens (recommended ≥ 8k)

How the fit score works

The score is rule-based and transparent. It reads what the Hugging Face Hub says about a model — its tags, pipeline tag, repository name and model type — with its parameter count and maximum context, and checks those signals against what each task needs: instruction tuning, code or reasoning training, vision, multilingual data, enough context, a suitable size.

Each of the 11 tasks weighs its criteria differently: RAG leans on context (32k tokens is ideal), on-device use on a small size. The overall score is the weighted mean out of 100, and a hard mismatch — an embedding model asked to chat, a text-only model asked to see — caps it at 25. From 80 a model is “built for this”, from 60 “workable”, from 40 “thin on paper”, and below that “the wrong tool”.

Questions

Which tasks are scored?
Chatbot and assistant, code generation, RAG and document Q&A, long-document summarisation, agents and tool use, reasoning and math, multilingual, vision and multimodal, embeddings and search, on-device and edge, and high-throughput serving.
Does a high score mean the model is good?
It means the model is built for the task, not that it is good at it. The score cannot see answer quality, grounding or hallucination rate — benchmarks and your own evaluation still decide between two models that both fit.
Why does an instruct model score higher for chat?
Base models are not trained to follow instructions, so the chat task gives instruction tuning its largest weight, 45%. The page detects it from tags such as conversational or chat, and from names containing Instruct, Chat or -it.
Can a capable model score lower than it deserves?
Yes. The rules only see what a model declares, so a model with sparse tags or a terse name loses points on signals it never states. Check the criteria list: each one shows what was found and what it was worth.