Feature Extraction
Transformers
Safetensors
English
qwen2
text-generation-inference
unsloth
text-embeddings-inference
Instructions to use jinesh90/qwen2.5-coder-sql-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinesh90/qwen2.5-coder-sql-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinesh90/qwen2.5-coder-sql-generator")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jinesh90/qwen2.5-coder-sql-generator") model = AutoModel.from_pretrained("jinesh90/qwen2.5-coder-sql-generator") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use jinesh90/qwen2.5-coder-sql-generator with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jinesh90/qwen2.5-coder-sql-generator to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jinesh90/qwen2.5-coder-sql-generator to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jinesh90/qwen2.5-coder-sql-generator to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jinesh90/qwen2.5-coder-sql-generator", max_seq_length=2048, )
(Trained with Unsloth)
Browse files
model-00002-of-00004.safetensors
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