Instructions to use jan-hq/supermario-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jan-hq/supermario-v1 with Transformers:

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

pipe = pipeline("text-generation", model="jan-hq/supermario-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jan-hq/supermario-v1")
model = AutoModelForCausalLM.from_pretrained("jan-hq/supermario-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jan-hq/supermario-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jan-hq/supermario-v1

SGLang

How to use jan-hq/supermario-v1 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 "jan-hq/supermario-v1" \
    --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": "jan-hq/supermario-v1",
		"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 "jan-hq/supermario-v1" \
        --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": "jan-hq/supermario-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jan-hq/supermario-v1 with Docker Model Runner:
```
docker model run hf.co/jan-hq/supermario-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Jan - Discord

Model Description

This model uses the DARE_TIES merge method.

NOTE: Due to the mismatch of architecture between Llama and Mistral, Magicoder-S-CL-7B layers will be skipped

base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-v0.1
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
  parameters:
    density: 0.8
    weight: 0.3
- model: Q-bert/MetaMath-Cybertron-Starling
  parameters:
    density: 0.8
    weight: 0.3
- model: ise-uiuc/Magicoder-S-CL-7B
  parameters:
    density: 0.6
    weight: 0.2
- model: AIDC-ai-business/Marcoroni-7B-v3
  parameters:
    density: 0.6
    weight: 0.2
parameters:
  int8_mask: true

Run this model

You can run this model using Jan Desktop on Mac, Windows, or Linux.

Jan is an open source, ChatGPT alternative that is:

💻 100% offline on your machine: Your conversations remain confidential, and visible only to you.
🗂️ An Open File Format: Conversations and model settings stay on your computer and can be exported or deleted at any time.
🌐 OpenAI Compatible: Local server on port 1337 with OpenAI compatible endpoints
🌍 Open Source & Free: We build in public; check out our Github

About Jan

Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.

Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.

Jan Model Merger

This is a test project for merging models.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here.

Metric	Value
Avg.	?
ARC (25-shot)	?
HellaSwag (10-shot)	?
MMLU (5-shot)	?
TruthfulQA (0-shot)	?
Winogrande (5-shot)	?
GSM8K (5-shot)	?

Acknowlegement

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.49
AI2 Reasoning Challenge (25-Shot)	27.73
HellaSwag (10-Shot)	25.83
MMLU (5-Shot)	27.04
TruthfulQA (0-shot)	47.27
Winogrande (5-shot)	49.09
GSM8k (5-shot)	0.00

Downloads last month: 53

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for jan-hq/supermario-v1

Quantizations

1 model

Collection including jan-hq/supermario-v1

Supermario

Collection

This is a test project for new merging methods (DARE, TIES, SLERP). • 5 items • Updated Jul 10, 2024

Evaluation results

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

27.730
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

25.830
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

27.040
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

47.270
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

49.090
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000