Translation
Transformers
Safetensors
qwen2
text-generation
sanskrit
transliteration
qwen
axolotl
iast
devanagari
bilingual
text-generation-inference
Instructions to use diabolic6045/Sanskrit-qwen-7B-Translate-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diabolic6045/Sanskrit-qwen-7B-Translate-v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="diabolic6045/Sanskrit-qwen-7B-Translate-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("diabolic6045/Sanskrit-qwen-7B-Translate-v2") model = AutoModelForCausalLM.from_pretrained("diabolic6045/Sanskrit-qwen-7B-Translate-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26c9f80458b01636f668d5ad6686dbb28717b8fa158e5040d7ecf84c2d20d299
- Size of remote file:
- 4.93 GB
- SHA256:
- db56f93aef044474188002d66d05165863c9a2600dd6f36c9eb722cf0f35fa7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.