M2M-100 is a multilingual encoder-decoder transformer for machine translation between
100 languages
— any direction, no English pivot required. The 418M variant has 12 encoder + 12 decoder layers (d=1024, 16 heads, FFN=4096, ReLU). Distributed under
MIT license
.
Files
File
Size
Notes
m2m100-418m-f16.gguf
935 MB
F16 weights (reference quality)
m2m100-418m-q8_0.gguf
502 MB
Q8_0 quantized (identical quality to F16 on test set)
m2m100-418m-q4_k.gguf
272 MB
Q4_K quantized (minor word choice differences)
Quick start
# 1. Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=OFF
cmake --build build -j
# 2. Pull model
huggingface-cli download cstr/m2m100-418m-GGUF m2m100-418m-q8_0.gguf --local-dir .
# 3. Translate (standalone)
./build/bin/crispasr --backend m2m100 -m m2m100-418m-q8_0.gguf \
--translate "Hello world, how are you today?" \
--source-lang en --target-lang de
# 4. ASR + translate pipeline
./build/bin/crispasr -m ggml-base.en.bin -f samples/jfk.wav \
--translate-model m2m100-418m-q8_0.gguf --target-lang de
Quality verification
All three quant levels produce correct translations:
Input (English)
F16
Q8_0
Q4_K
Hello world, how are you today?
Hallo Welt, wie bist du heute?
Hallo Welt, wie bist du heute?
Hallo Welt, wie bist du heute?
The president said he would not attend the meeting.
Le président a dit qu'il ne sera pas présent à la réunion.
...qu'il ne participerait pas...
...qu'il ne voulait pas assister...
Machine learning is changing the world.
El aprendizaje de máquina está cambiando el mundo.
(identical)
La aprendizaje... (minor)
Supported languages (100)
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
Architecture
Text → SentencePiece BPE tokenizer (128K vocab, 100 lang codes)
→ Source lang token (__en__) + text tokens + </s>
→ 12-layer transformer encoder (d=1024, 16 heads, FFN=4096, ReLU, pre-norm)
→ Sinusoidal positional embeddings (pre-computed)
→ 12-layer transformer decoder (self-attn + cross-attn + FFN)
→ Shared embedding LM head (tied weights)
→ Target lang forced as first decoder token
→ Greedy decode → translated text
Shared embedding: encoder, decoder, and LM head all use the same 128112×1024 table.
m2m100-418m-GGUF huggingface.co is an AI model on huggingface.co that provides m2m100-418m-GGUF's model effect (), which can be used instantly with this cstr m2m100-418m-GGUF model. huggingface.co supports a free trial of the m2m100-418m-GGUF model, and also provides paid use of the m2m100-418m-GGUF. Support call m2m100-418m-GGUF model through api, including Node.js, Python, http.
m2m100-418m-GGUF huggingface.co is an online trial and call api platform, which integrates m2m100-418m-GGUF's modeling effects, including api services, and provides a free online trial of m2m100-418m-GGUF, you can try m2m100-418m-GGUF online for free by clicking the link below.
cstr m2m100-418m-GGUF online free url in huggingface.co:
m2m100-418m-GGUF is an open source model from GitHub that offers a free installation service, and any user can find m2m100-418m-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of m2m100-418m-GGUF install, users can directly use m2m100-418m-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.