Summarization
Transformers
Safetensors
Malayalam
llama
text-generation
malayalam
dapt
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Navneeth017/sarvam1-dapt-general")
model = AutoModelForCausalLM.from_pretrained("Navneeth017/sarvam1-dapt-general", device_map="auto")Quick Links
sarvam1-dapt-general
sarvam-1 (2.5B) after general-world Malayalam DAPT (causal, 297.05M tokens). Intermediate checkpoint.
Part of the nlg_dapt_study corpus-controlled DAPT study for Malayalam summarisation. Central finding: documented DAPT (cultural or general-world, 23M–297M tokens) yields no statistically significant downstream gain on either mBART or sarvam-1. Private research artifact.
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Navneeth017/sarvam1-dapt-general")