PEFT
TensorBoard
Safetensors
llama
trl
sft
unsloth
Generated from Trainer
4-bit precision
bitsandbytes
Instructions to use stacklok/CodeLlama-7b-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use stacklok/CodeLlama-7b-hf with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codellama-7b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "stacklok/CodeLlama-7b-hf") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use stacklok/CodeLlama-7b-hf 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 stacklok/CodeLlama-7b-hf 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 stacklok/CodeLlama-7b-hf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for stacklok/CodeLlama-7b-hf to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="stacklok/CodeLlama-7b-hf", max_seq_length=2048, )
- Xet hash:
- 612b6daf5b2aa70202b509b2e2e2759c4805ebcbc2020e70ed0d7ca4b5025cdc
- Size of remote file:
- 160 MB
- SHA256:
- be4a3e0aa1a07c4e20115fab01fe7e2cd2bac0373409455d965e8efc05c122c9
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