buddhist-nlp/gemma-2-mitra-chat
gemma-2-mitra-chat
A multi-turn chat model for Buddhist studies from the Dharmamitra project: closed-book Buddhism Q&A plus a translation/refinement assistant for classical languages (Sanskrit, Tibetan, Buddhist Chinese, Pāli), speaking the standard gemma-2 chat protocol — it works out of the box with Ollama / llama.cpp multi-turn templates.
Built by full-parameter SFT of `buddhist-nlp/gemma2-mitra-base` (9.5B, Buddhist-domain continued pretraining) on ~10k examples: ~5k closed-book Buddhism Q&A (mined open-book, references removed so the knowledge is distilled into the weights) and ~5k translation/refine tasks (zh/sa/bo/pi, half with retrieved reference passages). Completion-only loss over full chat histories; EOS is <end_of_turn>, so turns close correctly in stock chat runtimes.
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("buddhist-nlp/gemma-2-mitra-chat")
model = AutoModelForCausalLM.from_pretrained(
"buddhist-nlp/gemma-2-mitra-chat", dtype=torch.bfloat16, device_map="cuda"
)
messages = [{"role": "user", "content":
"Translate into English: 'di skad bdag gis thos pa dus gcig na"}]
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=256)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))Tibetan may be given in Wylie transliteration (as in the mitra convention); Sanskrit and Pāli in IAST.
Training details
- Base:
buddhist-nlp/gemma2-mitra-base - Full-parameter SFT (TRL, completion-only loss, gemma-2 chat template), LR 1e-5, effective batch 64, cosine schedule, bf16; best checkpoint at step 220
- Data: combined closed-book Q&A + translation/refinement corpus (~10k multi-turn examples)
Related models
Part of the buddhist-nlp gemma-2 Mitra family; see also the newer Qwen3.5-based generation: `buddhist-nlp/mitra-qwen35-base`, `buddhist-nlp/mitra-qwen35-embedder`.
Citation
If you use this model, please cite the Dharmamitra project (https://dharmamitra.org).
