Instructions to use Volko76/Lucie-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Volko76/Lucie-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Volko76/Lucie-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Volko76/Lucie-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Volko76/Lucie-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Volko76/Lucie-7B-Instruct 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 Volko76/Lucie-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Volko76/Lucie-7B-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Volko76/Lucie-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Volko76/Lucie-7B-Instruct: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 Volko76/Lucie-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Volko76/Lucie-7B-Instruct: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 Volko76/Lucie-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Volko76/Lucie-7B-Instruct:Q4_K_M

Use Docker

docker model run hf.co/Volko76/Lucie-7B-Instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use Volko76/Lucie-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Volko76/Lucie-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Volko76/Lucie-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Volko76/Lucie-7B-Instruct:Q4_K_M

SGLang

How to use Volko76/Lucie-7B-Instruct 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 "Volko76/Lucie-7B-Instruct" \
    --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": "Volko76/Lucie-7B-Instruct",
		"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 "Volko76/Lucie-7B-Instruct" \
        --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": "Volko76/Lucie-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Volko76/Lucie-7B-Instruct with Ollama:
```
ollama run hf.co/Volko76/Lucie-7B-Instruct:Q4_K_M
```

Unsloth Studio

How to use Volko76/Lucie-7B-Instruct 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 Volko76/Lucie-7B-Instruct 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 Volko76/Lucie-7B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Volko76/Lucie-7B-Instruct to start chatting

Docker Model Runner
How to use Volko76/Lucie-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Volko76/Lucie-7B-Instruct:Q4_K_M
```

Lemonade

How to use Volko76/Lucie-7B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Volko76/Lucie-7B-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.Lucie-7B-Instruct-Q4_K_M

List all available models

lemonade list

Atomic Chat

Volko76 commited on Jan 15, 2025

Commit

0790fa4

verified ·

1 Parent(s): 3e2f68c

Update readme

Browse files

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -1,5 +1,10 @@
 ---
 license: apache-2.0
 datasets:
 - yahma/alpaca-cleaned
 - cmh/alpaca_data_cleaned_fr_52k
@@ -10,7 +15,7 @@ language:
 - en
 base_model:
 - OpenLLM-France/Lucie-7B
-pipeline_tag: text-generation
 ---
 # Model Card for Lucie-7B-Instruct
@@ -72,11 +77,8 @@ The model architecture and hyperparameters are the same as for [Lucie-7B](https:
 ### Test with ollama
 * Download and install [Ollama](https://ollama.com/download)
-* Download the [GGUF model](https://huggingface.co/OpenLLM-France/Lucie-7B-Instruct/resolve/main/Lucie-7B-q4_k_m.gguf)
-* Copy the [`Modelfile`](Modelfile), adpating if necessary the path to the GGUF file (line starting with `FROM`).
 * Run in a shell:
-    * `ollama create -f Modelfile Lucie`
-    * `ollama run Lucie`
 * Once ">>>" appears, type your prompt(s) and press Enter.
 * Optionally, restart a conversation by typing "`/clear`"
 * End the session by typing "`/bye`".

 ---
 license: apache-2.0
+tags:
+  - text-generation-inference
+  - transformers
+  - llama
+  - trl
 datasets:
 - yahma/alpaca-cleaned
 - cmh/alpaca_data_cleaned_fr_52k
 - en
 base_model:
 - OpenLLM-France/Lucie-7B
+pipeline_tag: text2text-generation
 ---
 # Model Card for Lucie-7B-Instruct
 ### Test with ollama
 * Download and install [Ollama](https://ollama.com/download)
 * Run in a shell:
+    * `ollama run hf.co/OpenLLM-France/Lucie-7B-Instruct`
 * Once ">>>" appears, type your prompt(s) and press Enter.
 * Optionally, restart a conversation by typing "`/clear`"
 * End the session by typing "`/bye`".