Instructions to use SupraLabs/Supra-Mini-v4-2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra-Mini-v4-2M with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra-Mini-v4-2M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-Mini-v4-2M")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-Mini-v4-2M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SupraLabs/Supra-Mini-v4-2M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra-Mini-v4-2M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Mini-v4-2M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra-Mini-v4-2M

SGLang

How to use SupraLabs/Supra-Mini-v4-2M 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 "SupraLabs/Supra-Mini-v4-2M" \
    --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": "SupraLabs/Supra-Mini-v4-2M",
		"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 "SupraLabs/Supra-Mini-v4-2M" \
        --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": "SupraLabs/Supra-Mini-v4-2M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SupraLabs/Supra-Mini-v4-2M with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra-Mini-v4-2M
```

LH-Tech-AI commited on 10 days ago

Commit

a5e8634

verified ·

1 Parent(s): 77bdd2a

Create train_tokenizer.py

Browse files

Files changed (1) hide show

train_tokenizer.py +25 -0

train_tokenizer.py ADDED Viewed

	@@ -0,0 +1,25 @@

+print("[*] Loading libraries...")
+from datasets import load_dataset
+from tokenizers import ByteLevelBPETokenizer
+from tqdm import tqdm
+dataset = load_dataset("HuggingFaceFW/fineweb-edu", "sample-10BT", split="train", streaming=True)
+def get_training_corpus():
+    dataset_iter = iter(dataset)
+    for _ in tqdm(range(1_000_000), desc="Feeding data"):
+        yield next(dataset_iter)["text"]
+tokenizer = ByteLevelBPETokenizer()
+print("[*] Training tokenizer...")
+tokenizer.train_from_iterator(
+    get_training_corpus(),
+    vocab_size=8192,
+    min_frequency=2,
+    show_progress=True,
+    special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
+)
+tokenizer.save_model(".", "custom_llama_tokenizer")
+print("[*] Tokenizer training complete!")