Instructions to use buddhist-nlp/mitra-qwen35-base-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buddhist-nlp/mitra-qwen35-base-stage2 with Transformers:

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

pipe = pipeline("text-generation", model="buddhist-nlp/mitra-qwen35-base-stage2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("buddhist-nlp/mitra-qwen35-base-stage2")
model = AutoModelForCausalLM.from_pretrained("buddhist-nlp/mitra-qwen35-base-stage2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use buddhist-nlp/mitra-qwen35-base-stage2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buddhist-nlp/mitra-qwen35-base-stage2"
# 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-qwen35-base-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/buddhist-nlp/mitra-qwen35-base-stage2

SGLang

How to use buddhist-nlp/mitra-qwen35-base-stage2 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-qwen35-base-stage2" \
    --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-qwen35-base-stage2",
		"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-qwen35-base-stage2" \
        --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-qwen35-base-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use buddhist-nlp/mitra-qwen35-base-stage2 with Docker Model Runner:
```
docker model run hf.co/buddhist-nlp/mitra-qwen35-base-stage2
```

mitra-qwen35-base-stage2

The 9B foundation model of the Dharmamitra stack: Qwen3.5-9B after large-scale domain adaptation to classical Buddhist literature. Stage 2 adds translation-specific capabilities on top of the stage-1 pretraining: bidirectional parallel translation training (Sanskrit↔English, Sanskrit↔German, and related classical pairs) plus full production translation/research turnarounds at 16k context. This is the model that buddhist-nlp/mitra-qwen35-embedder was finetuned from, released for research use as a starting point for Buddhist-NLP downstream tasks (translation, retrieval finetunes, QA, philology tooling).

Training

Two stages on top of Qwen3.5-9B:

Continued pretraining (~30B tokens, 8k context) on classical Buddhist corpora: Sanskrit, Tibetan (Wylie transliteration), Buddhist Chinese, and Pāli source texts with related secondary literature.
Stage-2 finetuning (16k context, 1 epoch) on a mixture of bidirectional parallel translation data (Sanskrit↔English, Sanskrit↔German, and related pairs), full production translation/research turnarounds, monolingual replay, and general instructions.

The result is an instruction-following model with strong classical-language competence in reading, translating, and discussing Buddhist source texts.

Conventions

Tibetan input/output is Wylie transliteration, not Tibetan script.
Sanskrit and Pāli are IAST romanization.
A chat template is bundled (tokenizer.apply_chat_template).

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("buddhist-nlp/mitra-qwen35-base-stage2")
model = AutoModelForCausalLM.from_pretrained(
    "buddhist-nlp/mitra-qwen35-base-stage2", dtype=torch.bfloat16, device_map="cuda"
)

messages = [{"role": "user", "content":
    "Translate into English: evaṃ mayā śrutam ekasmin samaye"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))