Instructions to use assemsabry/flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

llama.cpp

How to use assemsabry/flash with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

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

flash / config.json

assemsabry

(Trained with Unsloth)

0b2bd7c verified about 2 months ago

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	{
	"architectures": [
	"LlamaForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 128000,
	"torch_dtype": "float16",
	"eos_token_id": 128001,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 3072,
	"initializer_range": 0.02,
	"intermediate_size": 9216,
	"max_position_embeddings": 131072,
	"mlp_bias": false,
	"model_type": "llama",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"pad_token_id": 128004,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_parameters": {
	"factor": 8.0,
	"high_freq_factor": 4.0,
	"low_freq_factor": 1.0,
	"original_max_position_embeddings": 8192,
	"rope_theta": 500000.0,
	"rope_type": "llama3"
	},
	"tie_word_embeddings": false,
	"unsloth_version": "2026.3.18",
	"use_cache": false,
	"vocab_size": 128256
	}