Feature Extraction
Transformers
Safetensors
English
qwen2
bnb-my-repo
unsloth
code
qwen
qwen-coder
codeqwen
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use lainlives/Qwen2.5-Coder-7B-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lainlives/Qwen2.5-Coder-7B-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lainlives/Qwen2.5-Coder-7B-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lainlives/Qwen2.5-Coder-7B-bnb-4bit") model = AutoModel.from_pretrained("lainlives/Qwen2.5-Coder-7B-bnb-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
- Xet hash:
- d5eb7c723db8f1bd5876c2b4b1116f830b824d331753a844f59f4f932f11787f
- Size of remote file:
- 4.46 GB
- SHA256:
- 8dbb1844d70c2320336d7d22701e810b875e0768bb5cd88ca294ca1a2a4c3818
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.