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, AutoModelForCausalLM

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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tito-7B-slerp

Tito-7B-slerp is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: gordicaleksa/YugoGPT
        layer_range: [0, 32]
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.6
dtype: bfloat16

Results

Evaluations on Serbian LLM eval suite (or rather, performance and knowledge of Serbian):

	ARC-E	ARC-C	Hellaswag	BoolQ	Winogrande	OpenbookQA	PiQA	NQ Open	TriviaQA	Avg.
Zamfir-7B	51.85	32.25	46.03	75.59	62.59	26.00	66.81	16.09	36.11	45.92
Mustra-7B	52.95	33.70	45.89	77.55	64.17	30.60	67.25	15.40	34.84	46.93
Tito-7B	55.43	34.73	48.19	77.37	65.27	30.00	67.30	16.7	35.38	47.82
YugoGPT	57.79	34.73	49.89	69.45	64.56	28.20	72.03	15.82	36.14	47.62

Here, all benchmarks were done 0-shot, on the exception of NQ Open and TriviaQA which were done in 5-shot manner, in order to be comparable to Mistral paper.

If we try to replicate OpenLLM Leaderboard results on available Serbian datasets (running an appropriate amount of shots instead of 0), we get:

	ARC	Hellaswag	Winogrande	TruthfulQA	Avg.
Tito-7B	47.27	-	69.93	57.48	58.23
Perucac-7B	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

Detailed results can be found here

Metric	Tito	YugoGPT
Avg.	70.13	57.34
AI2 Reasoning Challenge (25-Shot)	68.09	58.10
HellaSwag (10-Shot)	86.38	81.44
MMLU (5-Shot)	64.01	60.68
TruthfulQA (0-shot)	57.01	36.60
Winogrande (5-shot)	81.69	76.56
GSM8k (5-shot)	63.61	30.70

Downloads last month: 86

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Stopwolf/Tito-7B-slerp

Quantizations

3 models

Spaces using Stopwolf/Tito-7B-slerp 15

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.090
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.380
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.010
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

57.010
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.690
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.610