Instructions to use Stopwolf/Tito-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Stopwolf/Tito-7B-slerp with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Stopwolf/Tito-7B-slerp")
model = AutoModelForMultimodalLM.from_pretrained("Stopwolf/Tito-7B-slerp")
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

vLLM

How to use Stopwolf/Tito-7B-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Stopwolf/Tito-7B-slerp

SGLang

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

Docker Model Runner
How to use Stopwolf/Tito-7B-slerp with Docker Model Runner:
```
docker model run hf.co/Stopwolf/Tito-7B-slerp
```

Stopwolf commited on Apr 22, 2024

Commit

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@@ -154,10 +154,12 @@ If we try to replicate OpenLLM Leaderboard results on available Serbian datasets
 |         | ARC   | Hellaswag | Winogrande | TruthfulQA | Avg.  |
 |---------|-------|-----------|------------|------------|-------|
 | Tito-7B | 47.27 |     -     |   69.93    |  **57.48** | 58.23 |
-| YugoGPT | 44.03 |     -     |   70.64    |    48.06   | 54.24 |
 | [Perucac-7B](https://huggingface.co/Stopwolf/Perucac-7B-slerp)  | **49.74** |   -   | **71.98** | 56.03 | **59.25** |
-| Llama3-8B | 42.24 |    -    |   61.25    |     51.08  | 51.52 |
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Stopwolf__Tito-7B-slerp)

 |         | ARC   | Hellaswag | Winogrande | TruthfulQA | Avg.  |
 |---------|-------|-----------|------------|------------|-------|
 | Tito-7B | 47.27 |     -     |   69.93    |  **57.48** | 58.23 |
 | [Perucac-7B](https://huggingface.co/Stopwolf/Perucac-7B-slerp)  | **49.74** |   -   | **71.98** | 56.03 | **59.25** |
+| YugoGPT | 44.03 |     -     |   70.64    |    48.06   | 54.24 |
+| Llama3-8B | 42.24 |    -    |   61.25    |    51.08   | 51.52 |
+| SambaLingo | 37.88 |   -    |   61.48    |    47.23   | 48.86 |
+Note that YugoGPT, Llama3 and SambaLingo are all base models, unlike Tito and Perucac.
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Stopwolf__Tito-7B-slerp)