Text Generation
Safetensors
GGUF
qwen2_5_vl
vision-language
multimodal
latex-ocr
image-to-text
qwen2.5-vl
lora
unsloth
ocr
mathematical-formulas
handwriting-recognition
lumichats
conversational
Instructions to use adityakum667388/lumichats-v1.2-7b-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use adityakum667388/lumichats-v1.2-7b-bnb-4bit 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 adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M # Run inference directly in the terminal: llama cli -hf adityakum667388/lumichats-v1.2-7b-bnb-4bit: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 adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf adityakum667388/lumichats-v1.2-7b-bnb-4bit: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 adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
Use Docker
docker model run hf.co/adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use adityakum667388/lumichats-v1.2-7b-bnb-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adityakum667388/lumichats-v1.2-7b-bnb-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adityakum667388/lumichats-v1.2-7b-bnb-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
- Ollama
How to use adityakum667388/lumichats-v1.2-7b-bnb-4bit with Ollama:
ollama run hf.co/adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use adityakum667388/lumichats-v1.2-7b-bnb-4bit with Docker Model Runner:
docker model run hf.co/adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
- Lemonade
How to use adityakum667388/lumichats-v1.2-7b-bnb-4bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull adityakum667388/lumichats-v1.2-7b-bnb-4bit:Q4_K_M
Run and chat with the model
lemonade run user.lumichats-v1.2-7b-bnb-4bit-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,017 Bytes
0172117 | 1 2 3 4 5 6 7 | {% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
You are a helpful assistant.<|im_end|>
{% endif %}<|im_start|>{{ message['role'] }}
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
{% endif %} |