Summarization
Transformers
Safetensors
GGUF
English
qwen3
text-generation
small-language-model
length-control
abstractive-summarization
saransh
text-generation-inference
Instructions to use M37labsorg/Saransh-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M37labsorg/Saransh-1.7B with Transformers:
# 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="M37labsorg/Saransh-1.7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("M37labsorg/Saransh-1.7B") model = AutoModelForCausalLM.from_pretrained("M37labsorg/Saransh-1.7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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