Instructions to use sthenno/tempesthenno-nuslerp-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sthenno/tempesthenno-nuslerp-001 with Transformers:

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

pipe = pipeline("text-generation", model="sthenno/tempesthenno-nuslerp-001")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sthenno/tempesthenno-nuslerp-001")
model = AutoModelForCausalLM.from_pretrained("sthenno/tempesthenno-nuslerp-001")
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 sthenno/tempesthenno-nuslerp-001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sthenno/tempesthenno-nuslerp-001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sthenno/tempesthenno-nuslerp-001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sthenno/tempesthenno-nuslerp-001

SGLang

How to use sthenno/tempesthenno-nuslerp-001 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 "sthenno/tempesthenno-nuslerp-001" \
    --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": "sthenno/tempesthenno-nuslerp-001",
		"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 "sthenno/tempesthenno-nuslerp-001" \
        --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": "sthenno/tempesthenno-nuslerp-001",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sthenno/tempesthenno-nuslerp-001 with Docker Model Runner:
```
docker model run hf.co/sthenno/tempesthenno-nuslerp-001
```

A newer version of this model is available: sthenno/tempesthenno-14b-nuslerp-0111

tempesthenno--nuslerp

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the NuSLERP merge method.

Models Merged

The following models were included in the merge:

/Users/sthenno/models/tempesthenno--converge-dtask
/Users/sthenno/models/tempesthenno--converge-breadcrumbs

Configuration

The following YAML configuration was used to produce this model:

name: tempesthenno--nuslerp
merge_method: nuslerp
tokenizer:
  source: /Users/sthenno/models/tempesthenno--converge-dtask
chat_template: "chatml"
dtype: float32
out_dtype: bfloat16
parameters:
  int8_mask: false
  normalize: true
  rescale: false
slices:
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [0, 8]
        parameters:
          weight: 0.65
          nuslerp_flatten: false
          nuslerp_row_wise: true
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [0, 8]
        parameters:
          weight: 0.35
          nuslerp_flatten: false
          nuslerp_row_wise: true
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [8, 16]
        parameters:
          weight: 0.60
          nuslerp_flatten: false
          nuslerp_row_wise: true
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [8, 16]
        parameters:
          weight: 0.40
          nuslerp_flatten: false
          nuslerp_row_wise: true
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [16, 24]
        parameters:
          weight: 0.55
          nuslerp_flatten: false
          nuslerp_row_wise: false
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [16, 24]
        parameters:
          weight: 0.45
          nuslerp_flatten: false
          nuslerp_row_wise: false
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [24, 32]
        parameters:
          weight: 0.50
          nuslerp_flatten: false
          nuslerp_row_wise: false
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [24, 32]
        parameters:
          weight: 0.50
          nuslerp_flatten: false
          nuslerp_row_wise: false
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [32, 40]
        parameters:
          weight: 0.45
          nuslerp_flatten: true
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [32, 40]
        parameters:
          weight: 0.55
          nuslerp_flatten: true
  - sources:
      - model: /Users/sthenno/models/tempesthenno--converge-dtask
        layer_range: [40, 48]
        parameters:
          weight: 0.40
          nuslerp_flatten: true
      - model: /Users/sthenno/models/tempesthenno--converge-breadcrumbs
        layer_range: [40, 48]
        parameters:
          weight: 0.60
          nuslerp_flatten: true

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	39.94
IFEval (0-Shot)	79.26
BBH (3-Shot)	51.04
MATH Lvl 5 (4-Shot)	31.72
GPQA (0-shot)	16.44
MuSR (0-shot)	13.88
MMLU-PRO (5-shot)	47.30

Downloads last month: 25

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for sthenno/tempesthenno-nuslerp-001

sometimesanotion/Qwen2.5-14B-Vimarckoso-v3

sthenno-com/miscii-14b-1225

Merge model

this model

Finetunes

2 models

Space using sthenno/tempesthenno-nuslerp-001 1

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

79.260
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

51.040
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

31.720
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

16.440
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

13.880
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

47.300