Instructions to use gate369/BrurryDog-7b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gate369/BrurryDog-7b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="gate369/BrurryDog-7b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("gate369/BrurryDog-7b-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("gate369/BrurryDog-7b-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gate369/BrurryDog-7b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gate369/BrurryDog-7b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gate369/BrurryDog-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gate369/BrurryDog-7b-v0.1

SGLang

How to use gate369/BrurryDog-7b-v0.1 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 "gate369/BrurryDog-7b-v0.1" \
    --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": "gate369/BrurryDog-7b-v0.1",
		"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 "gate369/BrurryDog-7b-v0.1" \
        --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": "gate369/BrurryDog-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gate369/BrurryDog-7b-v0.1 with Docker Model Runner:
```
docker model run hf.co/gate369/BrurryDog-7b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BrurryDog-7b-v0.1

BrurryDog-7b-v0.1 is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: udkai/Turdus
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: leveldevai/TurdusBeagle-7B
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: liminerity/Blur-7b-v1.21
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: udkai/Turdus
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "222gate/BrurryDog-7b-v0.1"
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.	74.24
AI2 Reasoning Challenge (25-Shot)	72.53
HellaSwag (10-Shot)	88.37
MMLU (5-Shot)	64.74
TruthfulQA (0-shot)	70.05
Winogrande (5-shot)	82.87
GSM8k (5-shot)	66.87

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for gate369/BrurryDog-7b-v0.1

leveldevai/TurdusBeagle-7B

liminerity/Blur-7b-v1.21

udkai/Turdus

Merge model

this model

Merges

3 models

Quantizations

1 model

Evaluation results

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

72.530
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.370
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.740
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

70.050
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

82.870
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.870