Instructions to use erfanzar/LinguaMatic-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use erfanzar/LinguaMatic-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="erfanzar/LinguaMatic-1B-GGUF",
	filename="LinguaMatic-1B-GGUF.Q2_K.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 erfanzar/LinguaMatic-1B-GGUF 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 erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf erfanzar/LinguaMatic-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf erfanzar/LinguaMatic-1B-GGUF: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 erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf erfanzar/LinguaMatic-1B-GGUF: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 erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf erfanzar/LinguaMatic-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/erfanzar/LinguaMatic-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use erfanzar/LinguaMatic-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "erfanzar/LinguaMatic-1B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "erfanzar/LinguaMatic-1B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/erfanzar/LinguaMatic-1B-GGUF:Q4_K_M

Ollama
How to use erfanzar/LinguaMatic-1B-GGUF with Ollama:
```
ollama run hf.co/erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
```

Unsloth Studio

How to use erfanzar/LinguaMatic-1B-GGUF 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 erfanzar/LinguaMatic-1B-GGUF 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 erfanzar/LinguaMatic-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for erfanzar/LinguaMatic-1B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use erfanzar/LinguaMatic-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/erfanzar/LinguaMatic-1B-GGUF:Q4_K_M
```

Lemonade

How to use erfanzar/LinguaMatic-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull erfanzar/LinguaMatic-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LinguaMatic-1B-GGUF-Q4_K_M

List all available models

lemonade list

LinguaMatic

LinguaMatic is an advanced AI model designed to handle a wide range of Natural Language Processing (NLP) tasks. With its powerful capabilities, LinguaMatic can assist with tasks such as text classification, sentiment analysis, language translation, question answering, and much more.

EasyDel

The model is finetuned Using a custom version of UltraChat on TPU-v4 POD using EasyDel

Prompting Method

LinguaMatic utilizes the OC prompting method to generate responses. This method, named after the friendly and intelligent llama, enhances the model's ability to engage in meaningful conversations. The prompt_model function provided below demonstrates how the llama2 prompting method is implemented:

def prompt_model(
        message: str,
        chat_history: Optional[List[str] | List[List[str]]] = None,
        system_prompt: Optional[str] = None
):
    if chat_history is None:
        chat_history = []
    system = f"<|system|>\n{system_prompt}</s>" if system_prompt is not None else ""
    ua = ""
    for user_input, response in chat_history:
        ua += f"<|user|>\n{user_input}</s>\n" + f"<|assistant|>\n{response}</s>\n"
    return system + ua + f"<|user|>\n{message}</s>\n<|assistant|>\n"

The prompt_model function takes a message as input, along with the chat_history and system_prompt. It generates a formatted text that includes the system prompt, user inputs, and the current message. This approach allows LinguaMatic to maintain context and provide more coherent and context-aware responses.

Contributing

We welcome contributions to enhance LinguaMatic's capabilities and improve its performance. If you encounter any issues or have suggestions for improvement, please feel free to submit a pull request or open an issue on EasyDel GitHub repository.

Downloads last month: 35

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

Dataset used to train erfanzar/LinguaMatic-1B-GGUF

Collection including erfanzar/LinguaMatic-1B-GGUF

LinguaMatic

Collection

LinguaMatic Models Matic in Natural Language Processing • 6 items • Updated Mar 2