---
title: SLM Arena
emoji: "🏟️"
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 6.19.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
license: apache-2.0
short_description: Compact model arena with GLM and GPT OSS commentary
models:
- HuggingFaceTB/SmolLM2-135M
- MaliosDark/Isabel-50M
- AxiomicLabs/GPT-X2-125M
- joelhenwang/OdinNext-138M-Base
- UniversalComputingResearch/Atom2.7m
- SupraLabs/Supra-1.5-50M-base-exp
- fromziro/Er-Tiny-1.3M
- fromziro/Er-Medium-12.5M
- veyra-ai/Veyra2-Apricot-50M-Base
- veyra-ai/Veyra2-30M-Base
- veyra-ai/Veyra2-15M-Base
- Harley-ml/Dillionv2-1.3M
- User01110/tinyLM-8M-exp-256
- Glint-Research/Glint-1.3
- AtomixLabs/AtomixS2-5M-v1.0
- BananaMind/MiniBananaMind-v4-9M
- MihaiPopa-1/CinnabarLM-1.4M-Base
- finnianx/Gros-Michel-90m-Base
- LH-Tech-AI/Spark-5M-Base-v4
- Eclipse-Senpai/KeyLM-75M
- GODELEV/Archaea-74M-V1.1
- Quazim0t0/Escarda-86M-Base
- StentorLabs/Stentor3-20M
- StentorLabs/Stentor3-50M
- jhu-clsp/ettin-decoder-17m
- jhu-clsp/ettin-decoder-32m
- jhu-clsp/ettin-decoder-68m
- jhu-clsp/ettin-decoder-150m
- 56m/Dumb-1.2-RC1
- Quazim0t0/Escarda-86M-Identity
- MultivexAI/Supra-1.6-50M-Instruct-Ultra-exp
- HuggingFaceTB/SmolLM2-135M-Instruct
- ThingAI/Quark-135m
- ThingAI/Quark-72M
- ThingAI/Quark-50m
- joelhenwang/OdinNext-138M-Instruct
- veyra-ai/Veyra2-30M-Instruct-Early
- MinimaLabs/KeyLM-75M-Instruct
tags:
- text-generation
- small-language-model
- model-arena
- blind-evaluation
- huggingface
---

# SLM Arena

Standalone Hugging Face Space for comparing **2 to 5** compact language models at once.

## What it does

- `Pick and See` lets you choose the models and keeps their identities visible while they generate.
- `Pick Blind` lets you choose the models manually, but the responses stay anonymous until `Reveal`.
- `Random Blind` samples hidden models from the full catalog, with options for:
  - allowing or blocking same-organization matches
  - enforcing a strict `<2x` size spread across the random group
- `Model Catalog` switches between `Base Models` and `Instruct Models` and keeps the dropdowns aligned with the active family.

## Commentary

After a run finishes, the Space can ask a provider-backed commentary model for neutral commentary:

- [`zai-glm-4.7`](https://inference-docs.cerebras.ai/resources/glm-47-migration) on Cerebras, using `CEREBRAS_TOKEN` and recommended by default
- [`openai/gpt-oss-120b`](https://console.groq.com/docs/model/openai/gpt-oss-120b) on Groq, using `GROQ_TOKEN`

The commentary prompt still uses these text-task sampling settings:

- `temperature=0.2`
- `top_p=1.0`

In `Pick and See`, commentary appears after the generations finish and the commentator call completes.

In `Pick Blind` and `Random Blind`, commentary stays locked until `Reveal` exposes the identities behind each response.
After `Reveal`, it appears in the same panel once the commentator call completes.
The commentary panel also has an on/off toggle and a provider selector for Cerebras or Groq.
If GLM 4.7 hits a rate limit, Cerebras currently lists it at 5 RPM, so switch to Groq's GPT OSS 120B.
Groq currently lists GPT OSS 120B at 30 RPM, or about 1 request every 2 seconds, which is usually enough for arena runs.

## Notes

- The catalog is preloaded at startup, but arena contestant weights still load lazily so the Space can keep the full roster without pinning every model at once.
- The catalog view is sorted by organization first and then by parameter count, and the dropdowns follow the active family.
- Commentary requests are sent through the provider OpenAI-compatible chat APIs using the selected repo secret.
- Some upstream repos are gated or use custom research code. Those models stay in the arena catalog, but generation still depends on the upstream repo exposing a working `transformers`-compatible loading path.