Instructions to use ChaoticNeutrals/Stanta-Lelemon-Maid-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Stanta-Lelemon-Maid-7B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Stanta-Lelemon-Maid-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Stanta-Lelemon-Maid-7B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Stanta-Lelemon-Maid-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChaoticNeutrals/Stanta-Lelemon-Maid-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/Stanta-Lelemon-Maid-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/Stanta-Lelemon-Maid-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/Stanta-Lelemon-Maid-7B

SGLang

How to use ChaoticNeutrals/Stanta-Lelemon-Maid-7B 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 "ChaoticNeutrals/Stanta-Lelemon-Maid-7B" \
    --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": "ChaoticNeutrals/Stanta-Lelemon-Maid-7B",
		"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 "ChaoticNeutrals/Stanta-Lelemon-Maid-7B" \
        --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": "ChaoticNeutrals/Stanta-Lelemon-Maid-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChaoticNeutrals/Stanta-Lelemon-Maid-7B with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/Stanta-Lelemon-Maid-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vision/multimodal capabilities:

If you want to use vision functionality:

You must use the latest versions of Koboldcpp.

To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo.

You can load the mmproj by using the corresponding section in the interface:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.79
AI2 Reasoning Challenge (25-Shot)	67.58
HellaSwag (10-Shot)	86.03
MMLU (5-Shot)	64.79
TruthfulQA (0-shot)	59.58
Winogrande (5-shot)	79.64
GSM8k (5-shot)	61.11

Downloads last month: 12

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ChaoticNeutrals/Stanta-Lelemon-Maid-7B

Base model

ChaoticNeutrals/KukulStanta-7B

Finetuned

(1)

this model

Merges

1 model

Quantizations

4 models

Spaces using ChaoticNeutrals/Stanta-Lelemon-Maid-7B 9

Evaluation results

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

67.580
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.030
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.790
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

59.580
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

79.640
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

61.110