Instructions to use Stark2008/VisFlamCat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Stark2008/VisFlamCat with Transformers:

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

pipe = pipeline("text-generation", model="Stark2008/VisFlamCat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Stark2008/VisFlamCat")
model = AutoModelForCausalLM.from_pretrained("Stark2008/VisFlamCat")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Stark2008/VisFlamCat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Stark2008/VisFlamCat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Stark2008/VisFlamCat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Stark2008/VisFlamCat

SGLang

How to use Stark2008/VisFlamCat 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 "Stark2008/VisFlamCat" \
    --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": "Stark2008/VisFlamCat",
		"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 "Stark2008/VisFlamCat" \
        --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": "Stark2008/VisFlamCat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Stark2008/VisFlamCat with Docker Model Runner:
```
docker model run hf.co/Stark2008/VisFlamCat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VisFlamCat

VisFlamCat is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: Nitral-AI/Visual-LaylelemonMaidRP-7B
    #no parameters necessary for base model
  - model: flammenai/flammen15-gutenberg-DPO-v1-7B
    parameters:
      density: 0.5
      weight: 0.5
  - model: Eric111/CatunaLaserPi
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Nitral-AI/Visual-LaylelemonMaidRP-7B
parameters:
  normalize: false
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Stark2008/VisFlamCat"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	21.16
IFEval (0-Shot)	43.66
BBH (3-Shot)	32.88
MATH Lvl 5 (4-Shot)	6.57
GPQA (0-shot)	5.37
MuSR (0-shot)	14.68
MMLU-PRO (5-shot)	23.82

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Stark2008/VisFlamCat

Eric111/CatunaLaserPi

flammenai/flammen15-gutenberg-DPO-v1-7B

Merge model

this model

Quantizations

1 model

Evaluation results

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

43.660
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

32.880
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

6.570
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

5.370
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

14.680
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

23.820