Instructions to use R4kSo1997/small100-onnx-int8_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use R4kSo1997/small100-onnx-int8_v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="R4kSo1997/small100-onnx-int8_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("R4kSo1997/small100-onnx-int8_v2") model = AutoModelForSeq2SeqLM.from_pretrained("R4kSo1997/small100-onnx-int8_v2") - Notebooks
- Google Colab
- Kaggle
SMaLL-100 ONNX int8 (movil offline)
Build movil-ready de alirezamsh/small100 para apps Flutter/onnxruntime.
ONNX
encoder_model_quantized.onnx(int8 ARM64 dynamic, per-channel)decoder_model_merged_quantized.onnx(int8 ARM64 dynamic, per-channel, merged: KV-cache + first-step)
Tokenizer
vocab.json,sentencepiece.bpe.model,tokenizer_config.json,special_tokens_map.jsontokenizer_data.json-> blob para Dart con vocab + scores + lang_tokens
Idiomas soportados
af, am, ar, ast, az, ba, be, bg, bn, br, bs, ca, ceb, cs, cy, da, de, el, en, es, et, fa, ff, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, ht, hu, hy, id, ig, ilo, is, it, ja, jv, ka, kk, km, kn, ko, lb, lg, ln, lo, lt, lv, mg, mk, ml, mn, mr, ms, my, ne, nl, no, ns, oc, or, pa, pl, ps, pt, ro, ru, sd, si, sk, sl, so, sq, sr, ss, su, sv, sw, ta, th, tl, tn, tr, uk, ur, uz, vi, wo, xh, yi, yo, zh, zu
Uso clave (importante)
SMaLL-100 funciona prepending el token del idioma DESTINO al input del encoder:
encoder_input = [__<tgt_lang>__, <texto_fuente_tokenizado>, </s>]
decoder_start = </s> (id=2)
Source: alirezamsh/small100
Target repo: R4kSo1997/small100-onnx-int8_v2
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