Instructions to use timpal0l/BeagleCatMunin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timpal0l/BeagleCatMunin with Transformers:

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

pipe = pipeline("text-generation", model="timpal0l/BeagleCatMunin")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("timpal0l/BeagleCatMunin")
model = AutoModelForMultimodalLM.from_pretrained("timpal0l/BeagleCatMunin")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use timpal0l/BeagleCatMunin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timpal0l/BeagleCatMunin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timpal0l/BeagleCatMunin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/timpal0l/BeagleCatMunin

SGLang

How to use timpal0l/BeagleCatMunin 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 "timpal0l/BeagleCatMunin" \
    --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": "timpal0l/BeagleCatMunin",
		"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 "timpal0l/BeagleCatMunin" \
        --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": "timpal0l/BeagleCatMunin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use timpal0l/BeagleCatMunin with Docker Model Runner:
```
docker model run hf.co/timpal0l/BeagleCatMunin
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a merge of timpal0l/Mistral-7B-v0.1-flashback-v2 and RJuro/munin-neuralbeagle-7b.

GGUF Version available Here

config.yaml

models:
  - model: timpal0l/Mistral-7B-v0.1-flashback-v2
    # No parameters necessary for base model
  - model: RJuro/munin-neuralbeagle-7b
    parameters:
      density: 0.53
      weight: 0.6
merge_method: dare_ties
base_model: timpal0l/Mistral-7B-v0.1-flashback-v2
parameters:
  int8_mask: true
dtype: bfloat16

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for timpal0l/BeagleCatMunin

RJuro/munin-neuralbeagle-7b-GGUF

timpal0l/Mistral-7B-v0.1-flashback-v2

Merge model

this model

Merges

5 models

Quantizations

1 model