Text Generation
Transformers
Safetensors
GGUF
English
llama
travel
tourism
india
hotels
restaurants
weather
maps
geolocation
railways
flights
bus-routes
nexuzy-lab
lora
conversational
text-generation-inference
Instructions to use davidx08/atithi-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidx08/atithi-ai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="davidx08/atithi-ai") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("davidx08/atithi-ai") model = AutoModelForCausalLM.from_pretrained("davidx08/atithi-ai", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use davidx08/atithi-ai with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf davidx08/atithi-ai # Run inference directly in the terminal: llama cli -hf davidx08/atithi-ai
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf davidx08/atithi-ai # Run inference directly in the terminal: llama cli -hf davidx08/atithi-ai
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 davidx08/atithi-ai # Run inference directly in the terminal: ./llama-cli -hf davidx08/atithi-ai
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 davidx08/atithi-ai # Run inference directly in the terminal: ./build/bin/llama-cli -hf davidx08/atithi-ai
Use Docker
docker model run hf.co/davidx08/atithi-ai
- LM Studio
- Jan
- vLLM
How to use davidx08/atithi-ai with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "davidx08/atithi-ai" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "davidx08/atithi-ai", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/davidx08/atithi-ai
- SGLang
How to use davidx08/atithi-ai with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "davidx08/atithi-ai" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "davidx08/atithi-ai", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "davidx08/atithi-ai" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "davidx08/atithi-ai", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use davidx08/atithi-ai with Ollama:
ollama run hf.co/davidx08/atithi-ai
- Unsloth Studio
How to use davidx08/atithi-ai 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 davidx08/atithi-ai 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 davidx08/atithi-ai to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for davidx08/atithi-ai to start chatting
- Docker Model Runner
How to use davidx08/atithi-ai with Docker Model Runner:
docker model run hf.co/davidx08/atithi-ai
- Lemonade
How to use davidx08/atithi-ai with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull davidx08/atithi-ai
Run and chat with the model
lemonade run user.atithi-ai-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
- Xet hash:
- d9032eec04ae5d2096a8f2cd6a3b4585c8fba0919007809c90adbde3f35ae179
- Size of remote file:
- 87.1 MB
- SHA256:
- a50a4ebcb2c918ac669cfdf5bfac141c714d433d20b0e811b49b436df18af2df
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