Instructions to use kevin009/llama342 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
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, )
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base_model: unsloth/Meta-Llama-3.1-8B-Instruct
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- en
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#
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
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license: apache-2.0
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-instruct
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pipeline_tag: text-generation
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tags:
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- lora
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- adapter
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- writing
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- CoT
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- unsloth
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## Model Details
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- Base Model: meta-llama/Llama-3.1-8B-instruct
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## Merger Configuration
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### Source Adapters
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All source adapters share the following configuration:
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- Rank (r): 16
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- Alpha: 16
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- Target Modules:
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- q_proj (Query projection)
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- k_proj (Key projection)
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- v_proj (Value projection)
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- o_proj (Output projection)
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- up_proj (Upsampling projection)
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- down_proj (Downsampling projection)
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- gate_proj (Gate projection)
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- The order of loading adapters may affect the final result
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- Equal weights were chosen to maintain balanced influence from each adapter
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- The merged adapter maintains the same architecture and rank as the original adapters
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- 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.
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