Instructions to use ArunKr/experimets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ArunKr/experimets with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ArunKr/experimets", filename="test_model.Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ArunKr/experimets with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArunKr/experimets:Q8_0 # Run inference directly in the terminal: llama-cli -hf ArunKr/experimets:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArunKr/experimets:Q8_0 # Run inference directly in the terminal: llama-cli -hf ArunKr/experimets:Q8_0
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 ArunKr/experimets:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ArunKr/experimets:Q8_0
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 ArunKr/experimets:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ArunKr/experimets:Q8_0
Use Docker
docker model run hf.co/ArunKr/experimets:Q8_0
- LM Studio
- Jan
- Ollama
How to use ArunKr/experimets with Ollama:
ollama run hf.co/ArunKr/experimets:Q8_0
- Unsloth Studio new
How to use ArunKr/experimets 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 ArunKr/experimets 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 ArunKr/experimets to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ArunKr/experimets to start chatting
- Docker Model Runner
How to use ArunKr/experimets with Docker Model Runner:
docker model run hf.co/ArunKr/experimets:Q8_0
- Lemonade
How to use ArunKr/experimets with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ArunKr/experimets:Q8_0
Run and chat with the model
lemonade run user.experimets-Q8_0
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Here’s a minimal, working example showing how you can create a Hugging Face model repo and upload files into it using Python.
1. Install dependencies
uv pip install huggingface_hub
2. Example Python script
from huggingface_hub import HfApi, create_repo, upload_file
import os
# 1. Authenticate (make sure HF_TOKEN is in your environment variables)
hf_token = os.getenv("HF_TOKEN")
if not hf_token:
raise EnvironmentError("Missing HF_TOKEN in environment variables.")
# 2. Define repo details
repo_name = "test-model-repo"
namespace = "your-username" # replace with your HF username/org
repo_id = f"{namespace}/{repo_name}"
# 3. Create repo (only first time, idempotent)
create_repo(repo_id, token=hf_token, repo_type="model", exist_ok=True)
print(f"✅ Repo created: https://huggingface.co/{repo_id}")
# 4. Upload files
api = HfApi()
# Example: upload a config.json file
upload_file(
path_or_fileobj="config.json", # local file path
path_in_repo="config.json", # where it will appear in repo
repo_id=repo_id,
token=hf_token
)
# Example: upload model weights
upload_file(
path_or_fileobj="pytorch_model.bin",
path_in_repo="pytorch_model.bin",
repo_id=repo_id,
token=hf_token
)
print("✅ Files uploaded successfully!")
3. Workflow
Get your token from Hugging Face settings → Access Tokens.
Save it in environment:
export HF_TOKEN=your_token_hereRun the script → it will:
- Create a model repo if not exists.
- Upload your files (e.g.,
config.json,pytorch_model.bin). - You’ll see them in
https://huggingface.co/your-username/test-model-repo.
👉 Do you want me to also show an example of uploading multiple files/folders at once (like the whole model directory with tokenizer, config, and weights)?
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docker model run hf.co/ArunKr/experimets:Q8_0