Instructions to use assemsabry/flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use assemsabry/flash with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="assemsabry/flash", filename="Flash-4B.F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use assemsabry/flash 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 assemsabry/flash:Q4_K_M # Run inference directly in the terminal: llama cli -hf assemsabry/flash:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf assemsabry/flash:Q4_K_M # Run inference directly in the terminal: llama cli -hf assemsabry/flash: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 assemsabry/flash:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf assemsabry/flash: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 assemsabry/flash:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf assemsabry/flash:Q4_K_M
Use Docker
docker model run hf.co/assemsabry/flash:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use assemsabry/flash with Ollama:
ollama run hf.co/assemsabry/flash:Q4_K_M
- Unsloth Studio
How to use assemsabry/flash 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 assemsabry/flash 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 assemsabry/flash to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for assemsabry/flash to start chatting
- Atomic Chat new
- Docker Model Runner
How to use assemsabry/flash with Docker Model Runner:
docker model run hf.co/assemsabry/flash:Q4_K_M
- Lemonade
How to use assemsabry/flash with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull assemsabry/flash:Q4_K_M
Run and chat with the model
lemonade run user.flash-Q4_K_M
List all available models
lemonade list
File size: 992 Bytes
0b2bd7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|end_of_text|>",
"from_slow": true,
"is_local": false,
"legacy": false,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"pad_token": "<|finetune_right_pad_id|>",
"padding_side": "left",
"tokenizer_class": "TokenizersBackend",
"chat_template": "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}{% elif message['role'] == 'assistant' %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}{% else %}{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] | trim + '<|eot_id|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
} |