Instructions to use SupraLabs/Supra-50M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra-50M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra-50M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-50M-Instruct")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-50M-Instruct")

llama-cpp-python

How to use SupraLabs/Supra-50M-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SupraLabs/Supra-50M-Instruct",
	filename="supra-50m-instruct-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SupraLabs/Supra-50M-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SupraLabs/Supra-50M-Instruct:F16
# Run inference directly in the terminal:
llama-cli -hf SupraLabs/Supra-50M-Instruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SupraLabs/Supra-50M-Instruct:F16
# Run inference directly in the terminal:
llama-cli -hf SupraLabs/Supra-50M-Instruct:F16

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 SupraLabs/Supra-50M-Instruct:F16
# Run inference directly in the terminal:
./llama-cli -hf SupraLabs/Supra-50M-Instruct:F16

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 SupraLabs/Supra-50M-Instruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SupraLabs/Supra-50M-Instruct:F16

Use Docker

docker model run hf.co/SupraLabs/Supra-50M-Instruct:F16

LM Studio
Jan

vLLM

How to use SupraLabs/Supra-50M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra-50M-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": "SupraLabs/Supra-50M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra-50M-Instruct:F16

SGLang

How to use SupraLabs/Supra-50M-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 "SupraLabs/Supra-50M-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": "SupraLabs/Supra-50M-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 "SupraLabs/Supra-50M-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": "SupraLabs/Supra-50M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SupraLabs/Supra-50M-Instruct with Ollama:
```
ollama run hf.co/SupraLabs/Supra-50M-Instruct:F16
```

Unsloth Studio new

How to use SupraLabs/Supra-50M-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 SupraLabs/Supra-50M-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 SupraLabs/Supra-50M-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SupraLabs/Supra-50M-Instruct to start chatting

Docker Model Runner
How to use SupraLabs/Supra-50M-Instruct with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra-50M-Instruct:F16
```

Lemonade

How to use SupraLabs/Supra-50M-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SupraLabs/Supra-50M-Instruct:F16

Run and chat with the model

lemonade run user.Supra-50M-Instruct-F16

List all available models

lemonade list

LH-Tech-AI commited on 3 days ago

Commit

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1 Parent(s): 39d052b

Update README.md

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README.md +13 -4

README.md CHANGED Viewed

@@ -39,19 +39,27 @@ For more details, the full code, configs and weights, please refer to [https://h
 ## 🚀 Inference
 ```python
 import torch
-from transformers import pipeline, AutoTokenizer
 # ── Config ────────────────────────────────────────────────────────────────────
-MODEL_ID = "SupraLabs/Supra-50M-Instruct/"
 MAX_NEW_TOKENS = 512
 # ── Load pipeline directly from HF ────────────────────────────────────────────
 print(f"[*] Loading SFT model and tokenizer from HF Hub ({MODEL_ID})...")
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 pipe = pipeline(
     "text-generation",
@@ -97,7 +105,8 @@ def generate(instruction: str, input_text: str = "", max_new_tokens: int = MAX_N
         repetition_penalty=1.15,
         pad_token_id=pipe.tokenizer.pad_token_id,
         eos_token_id=pipe.tokenizer.eos_token_id,
-        return_full_text=False
     )
     return result[0]['generated_text'].strip()

 ## 🚀 Inference
 ```python
+import os
+import warnings
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+warnings.filterwarnings("ignore", category=UserWarning, module="transformers")
 import torch
+from transformers import pipeline, AutoTokenizer, logging
+logging.set_verbosity_error()
 # ── Config ────────────────────────────────────────────────────────────────────
+MODEL_ID = "SupraLabs/Supra-50M-Instruct"
 MAX_NEW_TOKENS = 512
 # ── Load pipeline directly from HF ────────────────────────────────────────────
 print(f"[*] Loading SFT model and tokenizer from HF Hub ({MODEL_ID})...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, clean_up_tokenization_spaces=False)
 pipe = pipeline(
     "text-generation",
         repetition_penalty=1.15,
         pad_token_id=pipe.tokenizer.pad_token_id,
         eos_token_id=pipe.tokenizer.eos_token_id,
+        return_full_text=False,
+        generation_config=None
     )
     return result[0]['generated_text'].strip()