Instructions to use kevin009/llama342 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use kevin009/llama342 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 kevin009/llama342 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 kevin009/llama342 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kevin009/llama342 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="kevin009/llama342", max_seq_length=2048, )
Model Details
- Base Model: meta-llama/Llama-3.1-8B-instruct
Merger Configuration
Source Adapters
All source adapters share the following configuration:
- Rank (r): 16
- Alpha: 16
- Target Modules:
- q_proj (Query projection)
- k_proj (Key projection)
- v_proj (Value projection)
- o_proj (Output projection)
- up_proj (Upsampling projection)
- down_proj (Downsampling projection)
- gate_proj (Gate projection)
- The order of loading adapters may affect the final result
- Equal weights were chosen to maintain balanced influence from each adapter
- The merged adapter maintains the same architecture and rank as the original adapters
- While this adapter merges multiple fine-tunes, each component was developed as part of independent research efforts to explore and language model capabilities as part of R&D process.