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

Libraries

How to use samifares/LLM_FQL_LogScale with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="samifares/LLM_FQL_LogScale",
	filename="model-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use samifares/LLM_FQL_LogScale with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samifares/LLM_FQL_LogScale:F16
# Run inference directly in the terminal:
llama-cli -hf samifares/LLM_FQL_LogScale:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samifares/LLM_FQL_LogScale:F16
# Run inference directly in the terminal:
llama-cli -hf samifares/LLM_FQL_LogScale:F16

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 samifares/LLM_FQL_LogScale:F16
# Run inference directly in the terminal:
./llama-cli -hf samifares/LLM_FQL_LogScale:F16

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 samifares/LLM_FQL_LogScale:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samifares/LLM_FQL_LogScale:F16

Use Docker

docker model run hf.co/samifares/LLM_FQL_LogScale:F16

LM Studio
Jan
Ollama
How to use samifares/LLM_FQL_LogScale with Ollama:
```
ollama run hf.co/samifares/LLM_FQL_LogScale:F16
```

Unsloth Studio

How to use samifares/LLM_FQL_LogScale 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 samifares/LLM_FQL_LogScale 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 samifares/LLM_FQL_LogScale to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for samifares/LLM_FQL_LogScale to start chatting

Atomic Chat new
Docker Model Runner
How to use samifares/LLM_FQL_LogScale with Docker Model Runner:
```
docker model run hf.co/samifares/LLM_FQL_LogScale:F16
```

Lemonade

How to use samifares/LLM_FQL_LogScale with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samifares/LLM_FQL_LogScale:F16

Run and chat with the model

lemonade run user.LLM_FQL_LogScale-F16

List all available models

lemonade list

LLM_FQL_LogScale / README.md

samifares

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	---
	library_name: llama.cpp
	tags:
	- gguf
	- llama3
	- unsloth
	- logscale
	- humio
	---

	# LLM_FQL_LogScale (GGUF f16)

	This repo contains a GGUF `f16` export for use with llama.cpp / Ollama / LM Studio.

	## Files
	- `model-f16.gguf`

	## Notes
	- This is an `f16` GGUF. For smaller sizes, quantize to `q4_k_m` using llama.cpp `quantize`.