Instructions to use Chat-Error/Claude-Kimiko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Chat-Error/Claude-Kimiko with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Chat-Error/Mistral-Kimiko-CSFT") model = PeftModel.from_pretrained(base_model, "Chat-Error/Claude-Kimiko") - llama-cpp-python
How to use Chat-Error/Claude-Kimiko with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Chat-Error/Claude-Kimiko", filename="220.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 Chat-Error/Claude-Kimiko with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Chat-Error/Claude-Kimiko:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Chat-Error/Claude-Kimiko:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Chat-Error/Claude-Kimiko:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Chat-Error/Claude-Kimiko: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 Chat-Error/Claude-Kimiko:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Chat-Error/Claude-Kimiko: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 Chat-Error/Claude-Kimiko:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Chat-Error/Claude-Kimiko:Q4_K_M
Use Docker
docker model run hf.co/Chat-Error/Claude-Kimiko:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Chat-Error/Claude-Kimiko with Ollama:
ollama run hf.co/Chat-Error/Claude-Kimiko:Q4_K_M
- Unsloth Studio new
How to use Chat-Error/Claude-Kimiko 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 Chat-Error/Claude-Kimiko 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 Chat-Error/Claude-Kimiko to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Chat-Error/Claude-Kimiko to start chatting
- Docker Model Runner
How to use Chat-Error/Claude-Kimiko with Docker Model Runner:
docker model run hf.co/Chat-Error/Claude-Kimiko:Q4_K_M
- Lemonade
How to use Chat-Error/Claude-Kimiko with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Chat-Error/Claude-Kimiko:Q4_K_M
Run and chat with the model
lemonade run user.Claude-Kimiko-Q4_K_M
List all available models
lemonade list
Ctrl+K
- .ipynb_checkpoints
- checkpoint-100
- checkpoint-120
- checkpoint-140
- checkpoint-1488
- checkpoint-160
- checkpoint-180
- checkpoint-1984
- checkpoint-200
- checkpoint-220
- checkpoint-240
- checkpoint-2480
- checkpoint-260
- checkpoint-280
- checkpoint-300
- checkpoint-320
- checkpoint-340
- checkpoint-360
- checkpoint-380
- checkpoint-400
- checkpoint-420
- checkpoint-440
- checkpoint-460
- checkpoint-496
- checkpoint-60
- checkpoint-80
- checkpoint-992
- tmp-checkpoint-516
- 1.69 kB
- 4.37 GB xet
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- 336 MB xet
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