Instructions to use devch1013/YAILLAMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devch1013/YAILLAMA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("devch1013/YAILLAMA", dtype="auto") - llama-cpp-python
How to use devch1013/YAILLAMA with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="devch1013/YAILLAMA", filename="unsloth.BF16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use devch1013/YAILLAMA with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf devch1013/YAILLAMA:Q4_K_M # Run inference directly in the terminal: llama-cli -hf devch1013/YAILLAMA:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf devch1013/YAILLAMA:Q4_K_M # Run inference directly in the terminal: llama-cli -hf devch1013/YAILLAMA:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf devch1013/YAILLAMA:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf devch1013/YAILLAMA:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf devch1013/YAILLAMA:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf devch1013/YAILLAMA:Q4_K_M
Use Docker
docker model run hf.co/devch1013/YAILLAMA:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use devch1013/YAILLAMA with Ollama:
ollama run hf.co/devch1013/YAILLAMA:Q4_K_M
- Unsloth Studio new
How to use devch1013/YAILLAMA 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 devch1013/YAILLAMA 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 devch1013/YAILLAMA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for devch1013/YAILLAMA to start chatting
- Docker Model Runner
How to use devch1013/YAILLAMA with Docker Model Runner:
docker model run hf.co/devch1013/YAILLAMA:Q4_K_M
- Lemonade
How to use devch1013/YAILLAMA with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull devch1013/YAILLAMA:Q4_K_M
Run and chat with the model
lemonade run user.YAILLAMA-Q4_K_M
List all available models
lemonade list
Delete adapter_config.json
Browse files- adapter_config.json +0 -34
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0,
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"megatron_core": "megatron.core",
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"peft_type": "LORA",
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