Translation
Transformers
Safetensors
English
qwen2
text-generation
Eval Results (legacy)
text-generation-inference
Instructions to use westenfelder/Qwen2.5-Coder-7B-Instruct-NL2SH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use westenfelder/Qwen2.5-Coder-7B-Instruct-NL2SH 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="westenfelder/Qwen2.5-Coder-7B-Instruct-NL2SH")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("westenfelder/Qwen2.5-Coder-7B-Instruct-NL2SH") model = AutoModelForMultimodalLM.from_pretrained("westenfelder/Qwen2.5-Coder-7B-Instruct-NL2SH") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 663a2ee634d8b32fde2f9f951f9204b0d7e20b38833243c7d3a063ffcea87261
- Size of remote file:
- 4.33 GB
- SHA256:
- 1e53b9c7c9be1288d525473a079bcd57f7494cc877e0b16f66d59398b4e38975
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.