Instructions to use prithivMLmods/Luth-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Luth-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Luth-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Luth-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Luth-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Luth-Instruct-GGUF",
	filename="Luth-0.6B-Instruct-GGUF/Luth-0.6B-Instruct.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Luth-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Luth-Instruct-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 prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Luth-Instruct-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 prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Luth-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Luth-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Luth-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Luth-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Luth-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Luth-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Luth-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Luth-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Luth-Instruct-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Luth-Instruct-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 prithivMLmods/Luth-Instruct-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 prithivMLmods/Luth-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Luth-Instruct-GGUF to start chatting

How to use prithivMLmods/Luth-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Luth-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Luth-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Luth-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Luth-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Luth-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Luth-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Luth-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Luth-Instruct-GGUF

Luth-1.7B-Instruct is a French fine-tuned variant of the Qwen3-1.7B model, enhanced using the Luth-SFT dataset to significantly improve its capabilities in French instruction following, mathematics, and general knowledge while maintaining and even boosting its English performance. It was trained by full fine-tuning with Axolotl and later merged with the base Qwen3-1.7B, thus preserving its English competencies alongside marked improvements in French benchmarks. The model demonstrates strong performance on selected French and English benchmarks, including ifeval, gpqa-diamond, mmlu, math-500, arc-chall, and hellaswag, showing notable gains over comparable models in both languages. It is designed for tasks requiring bilingual proficiency with pronounced strength in French and is supported by available evaluation, training, and data scripts on GitHub. The model is suitable for instruction-following applications in contexts demanding enhanced French language understanding without compromising English language capabilities. It is openly accessible under an appropriate license for research and usage.

Model Name	Model Size	Download Link
Luth-1.7B-Instruct-GGUF	1.7B	Hugging Face
Luth-0.6B-Instruct-GGUF	0.6B	Hugging Face

Model Files

Luth-1.7B-Instruct

File Name	Quant Type	File Size
Luth-1.7B-Instruct.BF16.gguf	BF16	3.45 GB
Luth-1.7B-Instruct.F16.gguf	F16	3.45 GB
Luth-1.7B-Instruct.F32.gguf	F32	6.89 GB
Luth-1.7B-Instruct.Q2_K.gguf	Q2_K	778 MB
Luth-1.7B-Instruct.Q3_K_L.gguf	Q3_K_L	1 GB
Luth-1.7B-Instruct.Q3_K_M.gguf	Q3_K_M	940 MB
Luth-1.7B-Instruct.Q3_K_S.gguf	Q3_K_S	867 MB
Luth-1.7B-Instruct.Q4_0.gguf	Q4_0	1.05 GB
Luth-1.7B-Instruct.Q4_1.gguf	Q4_1	1.14 GB
Luth-1.7B-Instruct.Q4_K.gguf	Q4_K	1.11 GB
Luth-1.7B-Instruct.Q4_K_M.gguf	Q4_K_M	1.11 GB
Luth-1.7B-Instruct.Q4_K_S.gguf	Q4_K_S	1.06 GB
Luth-1.7B-Instruct.Q5_0.gguf	Q5_0	1.23 GB
Luth-1.7B-Instruct.Q5_1.gguf	Q5_1	1.32 GB
Luth-1.7B-Instruct.Q5_K.gguf	Q5_K	1.26 GB
Luth-1.7B-Instruct.Q5_K_M.gguf	Q5_K_M	1.26 GB
Luth-1.7B-Instruct.Q5_K_S.gguf	Q5_K_S	1.23 GB
Luth-1.7B-Instruct.Q6_K.gguf	Q6_K	1.42 GB
Luth-1.7B-Instruct.Q8_0.gguf	Q8_0	1.83 GB

Luth-0.6B-Instruct

File Name	Quant Type	File Size
Luth-0.6B-Instruct.BF16.gguf	BF16	1.2 GB
Luth-0.6B-Instruct.F16.gguf	F16	1.2 GB
Luth-0.6B-Instruct.F32.gguf	F32	2.39 GB
Luth-0.6B-Instruct.Q2_K.gguf	Q2_K	296 MB
Luth-0.6B-Instruct.Q3_K_L.gguf	Q3_K_L	368 MB
Luth-0.6B-Instruct.Q3_K_M.gguf	Q3_K_M	347 MB
Luth-0.6B-Instruct.Q3_K_S.gguf	Q3_K_S	323 MB
Luth-0.6B-Instruct.Q4_0.gguf	Q4_0	382 MB
Luth-0.6B-Instruct.Q4_1.gguf	Q4_1	409 MB
Luth-0.6B-Instruct.Q4_K.gguf	Q4_K	397 MB
Luth-0.6B-Instruct.Q4_K_M.gguf	Q4_K_M	397 MB
Luth-0.6B-Instruct.Q4_K_S.gguf	Q4_K_S	383 MB
Luth-0.6B-Instruct.Q5_0.gguf	Q5_0	437 MB
Luth-0.6B-Instruct.Q5_1.gguf	Q5_1	464 MB
Luth-0.6B-Instruct.Q5_K.gguf	Q5_K	444 MB
Luth-0.6B-Instruct.Q5_K_M.gguf	Q5_K_M	444 MB
Luth-0.6B-Instruct.Q5_K_S.gguf	Q5_K_S	437 MB
Luth-0.6B-Instruct.Q6_K.gguf	Q6_K	495 MB
Luth-0.6B-Instruct.Q8_0.gguf	Q8_0	639 MB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 223

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Luth-Instruct-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

kurakurai/Luth-1.7B-Instruct

Quantized

(4)

this model