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Hy-MT2-1.8B

Tencenthuggingface.co/tencent/Hy-MT2-1.8B

Hy-MT2-1.8B is a compact 1.8B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delimiter-based, contextual, glossary-based, and style-guided...

Open in Graph
data quality68

Updated 4 h ago · first seen 11 Sept 2026

model_01M294WVQPJ5AHF58ZXXBTFAKG

Overview

Identity

Canonical model
Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
Official checkpoints
official_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
Artifacts
1 quantization0 official · 1 third-partySeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
Provider deployments
1
API aliases
tencent/hy-mt2-1.8bIdentifiers under which providers and evaluators refer to this model.
Folded evaluation variants
0Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.

Openness

Open weights— weights downloadable under Apache-2.0; commercial use allowed; redistribution allowed; derivatives allowed; 4 dimensions unknown.

Weights downloadable under a permissive or Creative Commons licence allowing commercial use; code or data may be missing.

  • Weights

    Yes

  • Inference code

    —

  • Training code

    —

  • Training data

    —

  • Dataset

    —

  • Commercial use

    Yes

  • Redistribution

    Yes

  • Derivatives

    Yes

Licence: Apache License 2.0 (permissive · SPDX Apache-2.0 · stated as “apache-2.0”)

dimensions marked null are unknown, not false

Key facts

Release date

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 8 h agomedium

Model card
huggingface.co/tencent/Hy-MT2-1.8B

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium

Openrouter id

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Architecture

Architecture

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

Model type

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

Parameters

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium

Weights dtype

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

File size

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

Library name

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

Pipeline tag

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 13 h agomedium

Hugging Face repo

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Capabilities

Modalities

Modalities
text
Input
text
Output
text

Capabilities

  • Tool calling

    No

    OpenRouter public model & pricing listing · T2

  • Structured output

    No

    OpenRouter public model & pricing listing · T2

  • Reasoning

    Unavailable

  • Vision

    Unavailable

  • Audio

    Unavailable

  • Fine-tuning available

    Unavailable

Context window

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Max output

Source:OpenRouter public model & pricing listingT2observed 13 h agomedium

Languages

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium

Providers & Pricing1

All offers in the price terminal →
Provider deployments, cheapest output first
ProviderContextInput / 1MOutput / 1MStatusObservedSource
OpenRoutercheapest outputtencent/hy-mt2-1.8b8.19Kout 4.1Kactive1 h agosince 11 Sept 2026openrouter.aiT2

USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →

Price history

Step lines per provider; amber markers are recorded changes. Click a marker or a row for the evidence behind that price.

Output price · USD / 1M tokens 1 provider

Output price history of Hy-MT2-1.8B$0$0.05$0.10$0.15$0.20$0.25Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.177 · 11 Sept 2026
  • OpenRouter
  • OpenRouterfirst observed $0.17711 Sept 2026

Input price · USD / 1M tokens 1 provider

Input price history of Hy-MT2-1.8B$0$0.02$0.04$0.06Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.044 · 11 Sept 2026
  • OpenRouter
  • OpenRouterfirst observed $0.04411 Sept 2026

Hardware fit37

Estimated

37 of 37 device × quantization combinations fit.

Run locally: your machine →
Estimated hardware fit
HardwareQuantizationDevice memoryEst. memoryFits
Apple M3 Ultra4bit—1.7 GB est.Yes
Mac Studio (Apple M5 Ultra)4bit—1.7 GB est.Yes
Apple M2 Ultra4bit—1.7 GB est.Yes
Apple M1 Ultra4bit—1.7 GB est.Yes
Apple M3 Max4bit—1.7 GB est.Yes
Apple M4 Max4bit—1.7 GB est.Yes
Mac Studio (Apple M5 Max)4bit—1.7 GB est.Yes
MacBook Pro (Apple M5 Max)4bit—1.7 GB est.Yes
Apple M2 Max4bit—1.7 GB est.Yes
Apple M1 Max4bit—1.7 GB est.Yes
Apple M4 Pro4bit—1.7 GB est.Yes
Mac mini (Apple M5 Pro)4bit—1.7 GB est.Yes
MacBook Pro (Apple M5 Pro)4bit—1.7 GB est.Yes
Apple M3 Pro4bit—1.7 GB est.Yes
Apple M1 Pro4bit—1.7 GB est.Yes
Apple M2 Pro4bit—1.7 GB est.Yes
Apple M44bit—1.7 GB est.Yes
iMac (Apple M4)4bit—1.7 GB est.Yes
Mac mini (Apple M6)4bit—1.7 GB est.Yes
MacBook Air (Apple M5)4bit—1.7 GB est.Yes
MacBook Pro (Apple M5)4bit—1.7 GB est.Yes
Apple M24bit—1.7 GB est.Yes
Apple M34bit—1.7 GB est.Yes
Apple M14bit—1.7 GB est.Yes
NVIDIA GeForce RTX 30904bit24 GB1.7 GB est.Yes
NVIDIA GeForce RTX 40904bit24 GB1.7 GB est.Yes
NVIDIA GeForce RTX 50904bit32 GB1.7 GB est.Yes
NVIDIA A100 80GB4bit80 GB1.7 GB est.Yes
NVIDIA H100 SXM4bit80 GB1.7 GB est.Yes
NVIDIA H100 NVL4bit94 GB1.7 GB est.Yes
NVIDIA DGX Spark4bit128 GB1.7 GB est.Yes
NVIDIA H2004bit141 GB1.7 GB est.Yes
NVIDIA H200 NVL4bit141 GB1.7 GB est.Yes
NVIDIA B2004bit180 GB1.7 GB est.Yes
AMD Instinct MI300X4bit192 GB1.7 GB est.Yes
AMD Instinct MI325X4bit256 GB1.7 GB est.Yes
NVIDIA DGX B2004bit1,440 GB1.7 GB est.Yes
Assumptions (7)
  • Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead (or the observed artifact file size when one is recorded).
  • bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
  • KV cache: 2 × layers × kv_heads × head_dim × 2 bytes × context × batch when the architecture is known; otherwise 0.5 GB per 8 192 tokens (× batch), independent of architecture (GQA/MLA models need less).
  • A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
  • Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
  • Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
  • Multi-GPU: device memories are summed; interconnect bandwidth, tensor-parallel replication and pipeline bubbles are not modelled.

Lineage

Open in Graph →
Explicit derived_from / fine_tuned_from / distilled_from relations stated by sources; artifacts collapsed by kind.
DESCENDANTS 0 · ARTIFACTS1 quantizationartifacts · collapsed1 quantization — artifacts · collapsedHy-MT2-1.8B2.04B params · this modelHy-MT2-1.8B — 2.04B params · this model

    Versions & Artifacts1

    Version history

    Context windowfirst observation only

    11 Sept 2026current

    Licensefirst observation only

    11 Sept 2026current

    Max outputfirst observation only

    11 Sept 2026current

    Opennessfirst observation only

    11 Sept 2026current

    Parametersfirst observation only

    11 Sept 2026current

    Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.

    Artifacts 1

    quantization 1

    Papers1

    Timeline2

    Full timeline →
    • Release date changedModelHy-MT2-1.8BTencent

      Hy-MT2-1.8B: release date changed from 2026-05-11 to 2026-08-20

      Release date11 May 2026→20 Aug 2026openrouter
      openrouter.ai
    • Release date changedModelHy-MT2-1.8BTencent

      Hy-MT2-1.8B: release date changed from 2026-08-20 to 2026-05-11

      Release date20 Aug 2026→11 May 2026huggingface
      huggingface.co

    Change history

    Temporal, append-only claims: a new observation closes the previous claim instead of overwriting it. Rewind the record with the as-of picker.
    1 claims · 1 propertiesShow all properties

    Redistribution allowedredistribution_allowed1

    Claim history for Redistribution allowed
    ValueValid from → toStatusSourceConfidenceExtractor
    Yes → currentcurrentAI Atlas curated registry (YAML, versioned in git, every entry carries its source URL)T2highderived

    Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

    Provenance

    Attributed facts

    36

    Source tiers

    T236

    Freshest observation

    4 h ago

    Conflicts

    None

    Source documents 5

    Source documents
    SourceDocumentTypeTierLast observedSnapshots
    OpenRouter public model & pricing listingopenrouter.ai/api/v1/models catalogueT2· Quality secondary1 h ago10
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/models?author=tencent&p=0&sort=downloads listingT2· Quality secondary3 h ago7
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/tencent/Hy-MT2-1.8B/raw/main/README.md model_cardT2· Quality secondary6 h ago1
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/tencent/Hy-MT2-1.8B model_pageT2· Quality secondary7 h ago5
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/tencent/Hy-MT2-1.8B-GGUF model_pageT2· Quality secondary7 h ago5

    Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.

    Data quality (68/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →