Instructions to use buddhist-nlp/mitra-qwen35-2b-base-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buddhist-nlp/mitra-qwen35-2b-base-stage2 with Transformers:

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

pipe = pipeline("text-generation", model="buddhist-nlp/mitra-qwen35-2b-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-qwen35-2b-base-stage2")
model = AutoModelForCausalLM.from_pretrained("buddhist-nlp/mitra-qwen35-2b-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-qwen35-2b-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-qwen35-2b-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-qwen35-2b-base-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/buddhist-nlp/mitra-qwen35-2b-base-stage2

SGLang

How to use buddhist-nlp/mitra-qwen35-2b-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-qwen35-2b-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-qwen35-2b-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-qwen35-2b-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-qwen35-2b-base-stage2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use buddhist-nlp/mitra-qwen35-2b-base-stage2 with Docker Model Runner:
```
docker model run hf.co/buddhist-nlp/mitra-qwen35-2b-base-stage2
```

mitra-qwen35-2b-base-stage2 / README.md

snehrdich

initial release: mitra-qwen35-2b-base-stage2

719b7a1 verified 14 days ago

preview code

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metadata

license: apache-2.0
language:
  - sa
  - bo
  - zh
  - pi
  - en
  - de
tags:
  - buddhist-studies
  - sanskrit
  - tibetan
  - classical-chinese
  - pali
  - translation
library_name: transformers
pipeline_tag: text-generation

mitra-qwen35-2b-base-stage2

The compact (2B) member of the Dharmamitra Qwen3.5 family: Qwen3.5-2B after ~45B tokens of continued pretraining on classical Buddhist corpora (2 epochs, 8k context) and a stage-2 SFT that adds translation-specific capabilities — bidirectional parallel translation and production translation turnarounds (1.4 epochs).

Use it where the 9B mitra-qwen35-base-stage2 is too heavy; same conventions (Tibetan in Wylie, Sanskrit/Pāli in IAST), same chat template.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("buddhist-nlp/mitra-qwen35-2b-base-stage2")
model = AutoModelForCausalLM.from_pretrained(
    "buddhist-nlp/mitra-qwen35-2b-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))

Citation

If you use this model, please cite the Dharmamitra project (https://dharmamitra.org). A technical report is in preparation.