Instructions to use buddhist-nlp/mitra-bo-zh-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buddhist-nlp/mitra-bo-zh-tagger with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="buddhist-nlp/mitra-bo-zh-tagger")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("buddhist-nlp/mitra-bo-zh-tagger")
model = AutoModelForCausalLM.from_pretrained("buddhist-nlp/mitra-bo-zh-tagger", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use buddhist-nlp/mitra-bo-zh-tagger with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buddhist-nlp/mitra-bo-zh-tagger"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buddhist-nlp/mitra-bo-zh-tagger",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/buddhist-nlp/mitra-bo-zh-tagger

SGLang

How to use buddhist-nlp/mitra-bo-zh-tagger with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "buddhist-nlp/mitra-bo-zh-tagger" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buddhist-nlp/mitra-bo-zh-tagger",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "buddhist-nlp/mitra-bo-zh-tagger" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "buddhist-nlp/mitra-bo-zh-tagger",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use buddhist-nlp/mitra-bo-zh-tagger with Docker Model Runner:
```
docker model run hf.co/buddhist-nlp/mitra-bo-zh-tagger
```

mitra-bo-zh-tagger

A 9B-parameter model that turns raw classical Tibetan (Wylie) or raw, unpunctuated Buddhist Chinese into a full linguistic annotation in one pass:

sentence segmentation (one sentence per output line),
word segmentation at the dictionary-lexeme level (Tibetan) / Sanskrit-lemma level (Chinese),
a coarse part of speech on every word,
the words that together render one Sanskrit word or compound, bracketed (Chinese: with compound type),
for Chinese, punctuation restored (。，、：；？！ and quotation marks).

The model was trained by the MITRA project on top of the MITRA Qwen3.5-9B stage-2 base (Qwen3.5 architecture, continued pretraining on Buddhist Sanskrit, Tibetan, Chinese and parallel data). The convenient way to use it is the Python package dharmamitra/mitra-bo-zh-tagger, which handles chunking, Unicode/Wylie conversion, validation and conversion into structured records.

Input / output

Plain-text prompt (no chat template). The instruction line is fixed and must be given verbatim; the package's mitra_tagger.prompts module contains both strings.

<instruction>
TIBETAN: de nas tshe dang ldan pa kun dga' bos bcom ldan 'das la 'di skad ces gsol to/ /
OUTPUT:
[de_nas/A] [tshe_dang_ldan_pa/J] [kun_dga'_bo/P] +s/C [bcom_ldan_'das/N] la/C 'di_skad/A ces/L [gsol/V] to/L ||/p

<instruction>
CHINESE: 爾時世尊告諸比丘汝等當知一切諸法皆悉無常苦空無我若能如是觀者則得解脫
OUTPUT:
爾/R 時/N 世尊/N 告/V 諸/L 比丘/N ：/p 「/p 汝/R 等/L 當/X 知/V ，/p [一切/N 諸/L 法/N]=K 皆/A 悉/A 無常/J 、/p 苦/J 、/p 空/J 、/p [無/V 我/R]=B 。/p
若/S 能/X 如/V 是/R 觀/V 者/L ，/p 則/K 得/X 解脫/V 。/p 」/p

Format: tokens WORD/POS, punctuation MARK/p, Sanskrit units [ ... ]. Tibetan syllables of one word are joined with _; an affix written attached in the input ('i, s, r, 'o, 'am, 'ang) becomes its own token +affix; shad runs become |/p or ||/p. Chinese units carry =T tatpuruṣa, =K karmadhāraya, =D dvandva, =B bahuvrīhi, =A avyayībhāva, =O other.

Tibetan tags: N noun, P proper noun, V verb, G verbal noun, J adjective, A adverb, M numeral, R pronoun, D determiner/demonstrative/plural, X negation, C case particle, K clause particle, L clitic or final particle, I interjection, p punctuation. Chinese tags: N noun, P proper noun, V verb, J adjective, A adverb, R pronoun, M numeral, X auxiliary, C adposition, K coordinating conjunction, S subordinating conjunction, L particle, I interjection, p punctuation.

Input limits used in training: Tibetan sentences and paragraphs of a few sentences (targets up to ~1,000 characters of Wylie); Chinese windows of up to 200 characters. Longer texts should be chunked accordingly.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

m = "buddhist-nlp/mitra-bo-zh-tagger"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, dtype=torch.bfloat16, device_map="auto").eval()

from mitra_tagger.prompts import build_prompt   # pip install git+https://github.com/dharmamitra/mitra-bo-zh-tagger
prompt = build_prompt("若能如是觀者則得解脫", "zh")
ids = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=1024, do_sample=False)
print(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True))

Or simply pip install git+https://github.com/dharmamitra/mitra-bo-zh-tagger and mitra-tag --lang zh file.txt. Runs on CUDA (bfloat16, ~19 GB), Apple Silicon (float16 via MPS, 24 GB+ unified memory) and CPU.

Quality

Held-out test set, 100 items per language, against Gemini-corrected gold annotations:

	Tibetan	Chinese
Word boundary F1	0.95	0.94
POS accuracy (matched words)	0.94	0.94
Sentence boundary F1	0.87	0.81
Punctuation position F1	–	0.79
Sanskrit unit / compound F1	0.65	0.52
Output reproduces input	99%	86%

Sanskrit-unit bracketing is the weakest layer (mostly under-marking). Chinese round-trip failures on 200-character windows are single-character slips; sentence-level input is more robust.

Limitations

The gold data is model-corrected, not human-checked. The model has seen Buddhist canonical text only; other registers of classical Chinese or Tibetan will degrade.

Citation

Please refer to the Dharmamitra project and this model card when using the tagger. Publication under preperation!

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for buddhist-nlp/mitra-bo-zh-tagger

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

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