Instructions to use ltg/normistral-7b-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ltg/normistral-7b-scratch with Transformers:

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

pipe = pipeline("text-generation", model="ltg/normistral-7b-scratch")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ltg/normistral-7b-scratch")
model = AutoModelForCausalLM.from_pretrained("ltg/normistral-7b-scratch", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ltg/normistral-7b-scratch 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 ltg/normistral-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ltg/normistral-7b-scratch:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ltg/normistral-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ltg/normistral-7b-scratch: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 ltg/normistral-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ltg/normistral-7b-scratch: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 ltg/normistral-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ltg/normistral-7b-scratch:Q4_K_M

Use Docker

docker model run hf.co/ltg/normistral-7b-scratch:Q4_K_M

LM Studio
Jan

vLLM

How to use ltg/normistral-7b-scratch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ltg/normistral-7b-scratch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ltg/normistral-7b-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ltg/normistral-7b-scratch:Q4_K_M

SGLang

How to use ltg/normistral-7b-scratch 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 "ltg/normistral-7b-scratch" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ltg/normistral-7b-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ltg/normistral-7b-scratch" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ltg/normistral-7b-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use ltg/normistral-7b-scratch with Ollama:
```
ollama run hf.co/ltg/normistral-7b-scratch:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use ltg/normistral-7b-scratch with Docker Model Runner:
```
docker model run hf.co/ltg/normistral-7b-scratch:Q4_K_M
```

Lemonade

How to use ltg/normistral-7b-scratch with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ltg/normistral-7b-scratch:Q4_K_M

Run and chat with the model

lemonade run user.normistral-7b-scratch-Q4_K_M

List all available models

lemonade list

Atomic Chat

davda54 commited on Feb 24

Commit

61599bf

verified ·

1 Parent(s): 2f3a170

Update README.md

Browse files

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README.md +25 -0

README.md CHANGED Viewed

@@ -373,4 +373,29 @@ model = AutoModelForCausalLM.from_pretrained(
     load_in_8bit=True,
     torch_dtype=torch.bfloat16
 )
 ```

     load_in_8bit=True,
     torch_dtype=torch.bfloat16
 )
+```
+### Citation
+```bibtex
+@inproceedings{samuel-etal-2025-small,
+    title = "Small Languages, Big Models: {A} Study of Continual Training on Languages of {Norway}",
+    author = "Samuel, David  and
+      Mikhailov, Vladislav  and
+      Velldal, Erik  and
+      {\O}vrelid, Lilja  and
+      Charpentier, Lucas Georges Gabriel  and
+      Kutuzov, Andrey  and
+      Oepen, Stephan",
+    editor = "Johansson, Richard  and
+      Stymne, Sara",
+    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
+    month = mar,
+    year = "2025",
+    address = "Tallinn, Estonia",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2025.nodalida-1.61/",
+    pages = "573--608",
+    ISBN = "978-9908-53-109-0"
+}
 ```