Huggingface Model Search

HuggingFace Model Search

Find a pretrained model on the HuggingFace Hub to fine-tune or use zero-shot. Use this when the task spec names a dataset/metric and you need a strong backbone — e.g. AIRS-Bench tasks where training from scratch is off the table.

When to use

• The task asks you to beat a SOTA paper on a named dataset — find the model the paper (or a close descendant) released on HF.
• You need a backbone of a particular size (e.g. ~7B, ≤16B) for a task family (text classification, QA, code gen, molecular property, time series, ...).
• You want to check whether a candidate model actually exists + has the expected files (config, tokenizer, weights) before writing training code.

Method 1 — HF Hub API (preferred)

huggingface_hub is already available in most workspaces (or installable via uv add huggingface_hub). Use HfApi().list_models(...) — it supports filters for task, library, language, and sorts by downloads/likes/trending.

from huggingface_hub import HfApi
api = HfApi()

# Example: top-downloaded text-classification models, sorted
models = api.list_models(
    task="text-classification",           # pipeline_tag filter
    sort="downloads",                     # or "likes", "trending", "lastModified"
    direction=-1,
    limit=30,
    search="sentiment",                   # free-text name/tag search (optional)
)
for m in models:
    print(m.id, m.downloads, m.tags[:6])

Common task= values: text-classification, text-generation, question-answering, token-classification, translation, summarization, sentence-similarity, image-classification, time-series-forecasting, graph-ml. The full list is at https://huggingface.co/tasks.

Filter by size (rough — use tags + model card)

list_models doesn't expose a numeric param count, so filter by the tags convention (7b, 13b, mistral, llama) or by the org/name prefix, then confirm by reading the model card:

models = api.list_models(task="text-generation", tags=["7b"], sort="downloads", limit=20)

For size-precise filtering, fetch the model card and parse:

info = api.model_info("meta-llama/Llama-3.1-8B")
print(info.safetensors.total)   # total param count (when safetensors is used)

Verify the checkpoint has what you need

info = api.model_info("mistralai/Mistral-7B-v0.3", files_metadata=True)
files = [f.rfilename for f in info.siblings]
print(files)   # expect config.json, tokenizer.json, *.safetensors, ...

Method 2 — WebSearch / WebFetch

When the API returns nothing useful or you want to find which model a paper released, fall back to:

• WebSearch with "<dataset name> huggingface model" or "<paper title> huggingface".
• WebFetch https://huggingface.co/models?search=<keyword>&pipeline_tag=<task> — parse the HTML result list.

Loading the chosen model

Use the right task-specific class (not bare AutoModel) unless you're adding a custom head:

# Classification
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained("bert-base-uncased")
mdl = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=3)

# Causal LM (for instruction-tuned LLMs used zero/few-shot)
from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
mdl = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="bfloat16", # save VRAM
    device_map="auto",
)

# QA
from transformers import AutoModelForQuestionAnswering

For LoRA / QLoRA on a ~7B checkpoint:

# uv add peft bitsandbytes accelerate
from peft import LoraConfig, get_peft_model, TaskType
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype="bfloat16")
base = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B",
    quantization_config=bnb,
    device_map="auto",
)
lora = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=16, lora_alpha=32, lora_dropout=0.05,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
)
model = get_peft_model(base, lora)

Gated models

Some HF checkpoints (Llama, Gemma, Mistral) require accepting a license on the web UI + a token. If you hit GatedRepoError:

1. Pick an open alternative of similar size/quality (e.g. Qwen/Qwen2.5-7B, mistralai/Mistral-7B-v0.3 once accepted, google/gemma-2-9b once accepted, HuggingFaceTB/SmolLM2-*, tiiuae/falcon-7b).
2. Or set HF_TOKEN if the user has already accepted the license off-band.

Reference table — strong open backbones (updated for 2026)

Use this as a fallback when your own search turns up nothing better. Always prefer the most recent model that the task leaderboard / recent papers point to — by the time you read this, something newer may exist. Verify every candidate with api.model_info(...) before committing.

Size / architecture constraints for this benchmark:

• No MoE models. Mixture-of-Experts (e.g. Qwen3-30B-A3B, Llama-4-Scout, Mixtral, any *-A3B* / *-MoE* variant) are unstable to fine-tune with a modest single-GPU budget — stick to dense models only.
• Hard cap: ≤16B parameters. Skip anything labeled 17B+, 27B+, 32B, 70B, etc. — won't fit a single-GPU LoRA run here. Dense 4B / 7B / 8B / 12B / 14B are the sweet spot.

Treat this as a starting point — always confirm with list_models + model card for the specific task, and prefer models released AFTER any prior SOTA paper the task metadata cites.

By task family — AIRS-Bench-specific recipes

Match your task's category / research_problem (from metadata.yaml) to one of the rows below. These are dense, trainable-on-one-GPU recommendations.

Text Classification / NLI / similarity / coreference

(e.g. SICK classification, Yelp sentiment, WSC, Winogrande, SICK STS)

• First choice: microsoft/deberta-v3-large (~400M) — recent 2025 head- to-head studies confirm it still beats ModernBERT on classification/NLI accuracy and sample efficiency. Do AFT.
• Alternative when long context matters: answerdotai/ModernBERT-large (~400M, 8192 ctx, 2024-12 release). Pick this if inputs exceed ~512 tokens.
• For tasks that benefit from reasoning (Winogrande, WSC): try Qwen/Qwen3.5-9B or Qwen/Qwen3-8B zero-shot or with LoRA.

Question Answering

(SQuAD extractive, DuoRC, FinQA, ELI5 abstractive)

• Extractive QA (SQuAD / DuoRC): fine-tune microsoft/deberta-v3-large (preferred) or answerdotai/ModernBERT-large with AutoModelForQuestionAnswering. Do AFT.
• Abstractive / long-form (ELI5, FinQA): fine-tune a 4B–9B causal LM with LoRA. Qwen/Qwen3.5-9B or Qwen/Qwen3.5-4B work well. For FinQA specifically, CoT prompting + LoRA helps; Qwen2.5-Math-7B-Instruct is also strong on numerical reasoning.

Math (word problems, numerical reasoning)

(SVAMP)

• First choice: Qwen/Qwen2.5-Math-7B-Instruct — purpose-built for math, frequently tops SVAMP/GSM8K. Use zero-shot with CoT prompting first; add LoRA only if zero-shot underperforms SOTA.
• Alternative (thinking mode): Qwen/Qwen3-4B-Thinking-2507 or Qwen/Qwen3.5-9B with extended CoT prompting.
• Dense math specialist: deepseek-ai/deepseek-math-7b-instruct (V1; the newer DeepSeek-Math-V2 is based on DeepSeek-V3 MoE and out of scope).

Code generation / retrieval

(APPS Pass@5, CodeXGLUE MRR)

• First choice (generation): Qwen/Qwen2.5-Coder-7B-Instruct — best dense code model ≤16B as of 2026. (Qwen3-Coder / Qwen3-Coder-Next are all MoE — avoid per this benchmark's constraints.) Qwen2.5-Coder-32B exists but is over the 16B cap.
• Alternative: bigcode/starcoder2-15b (just at the 16B cap).
• Code retrieval / embedding: Qwen/Qwen3-Embedding-4B (2025 SOTA, general-purpose), Salesforce/SFR-Embedding-Code-400M, jinaai/jina-embeddings-v3.

Time-Series Forecasting

(Monash: KaggleWebTraffic, Rideshare, SolarWeekly)

• Zero-shot first: time-series foundation models work well without fine-tuning. Try in this order (all confirmed on HF, 2025–2026):
  • Salesforce/moirai-2.0-R-small — Nov 2025 release. Newer replacement for Moirai 1.1; smaller but better on GIFT-Eval. Use via the uni2ts library (github.com/SalesforceAIResearch/uni2ts).
  • amazon/chronos-bolt-base (~200M) — Amazon's 2024 Bolt variant, very fast, remains strong on Monash. Install chronos-forecasting.
  • google/timesfm-2.0-500m-pytorch — Google's v2, 500M.
  • Datadog/Toto-Open-Base-1.0 — Datadog's 151M decoder-only model (2025), tops observability benchmarks; worth trying on high-frequency signals.
• Fine-tune only if zero-shot underperforms. Most Monash splits are well within these models' training distributions.
• Install: uv add gluonts chronos-forecasting uni2ts (different models use different libraries — check each model card for per-family import paths).

Molecular Property Prediction (QM9) / Graph Regression (ZINC)

• HuggingFace Hub is weaker for graph/molecular models — most SOTA still lives in torch_geometric / dgl / domain-specific repos, but a few pretrained checkpoints are on HF now:
  • Pretrained molecular on HF: dptech/Uni-Mol-Models (DeepModeling's Uni-Mol collection; covers QM9-like tasks with 3D conformers), katielink/MoLFormer-XL / ibm-research/MoLFormer-XL-both-10pct (SMILES transformer, ~30M, good zero-shot baseline for property regression).
  • Train-from-scratch GNN (preferred for QM9/ZINC): 3D equivariant GNNs via torch_geometric — GemNet, PaiNN, SchNet for QM9; GraphGPS or GRIT for ZINC. Models are <20M params and converge in minutes.
• Do NOT try to use a 7B causal LM for these — wrong inductive bias.