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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

mitra-qwen35-it

The general-purpose instruction/chat model of the Dharmamitra Qwen3.5 family: a multi-turn SFT of mitra-qwen35-base-stage2 for Buddhist-studies conversation — closed-book Buddhism Q&A plus translation and translation-refinement assistance for classical languages (Sanskrit, Tibetan, Buddhist Chinese, Pāli).

This is the recommended entry point if you want to talk to the mitra family; use the stage-2 base for raw translation pipelines and the embedder for retrieval.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("buddhist-nlp/mitra-qwen35-it")
model = AutoModelForCausalLM.from_pretrained(
    "buddhist-nlp/mitra-qwen35-it", dtype=torch.bfloat16, device_map="cuda"
)
messages = [{"role": "user", "content":
    "What is the difference between śamatha and vipaśyanā?"}]
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=512)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Tibetan may be given in Wylie transliteration; Sanskrit and Pāli in IAST.

Training details

Base: buddhist-nlp/mitra-qwen35-base-stage2 (9B; ~30B tokens Buddhist CPT
- translation-capability SFT)
Multi-turn SFT (TRL, completion-only loss over full chat histories), LR 1e-5, on a combined corpus of closed-book Buddhism Q&A (knowledge distilled into weights) and translation/refinement tasks across zh/sa/bo/pi

Citation

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

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for buddhist-nlp/mitra-qwen35-it

Base model

buddhist-nlp/mitra-qwen35-base-stage2

Finetuned

(1)

this model

Collection including buddhist-nlp/mitra-qwen35-it

Mitra Qwen3.5 (2026)

Collection

Dharmamitra's Qwen3.5-9B family for classical Buddhist literature: CPT base, instruction SFT, and retrieval embedder. Trained in July 2026. • 6 items • Updated about 6 hours ago