Instructions to use davzoku/moecule-1.1b-m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davzoku/moecule-1.1b-m2 with Transformers:

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

pipe = pipeline("text-generation", model="davzoku/moecule-1.1b-m2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davzoku/moecule-1.1b-m2")
model = AutoModelForCausalLM.from_pretrained("davzoku/moecule-1.1b-m2", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davzoku/moecule-1.1b-m2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davzoku/moecule-1.1b-m2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davzoku/moecule-1.1b-m2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davzoku/moecule-1.1b-m2

SGLang

How to use davzoku/moecule-1.1b-m2 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 "davzoku/moecule-1.1b-m2" \
    --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": "davzoku/moecule-1.1b-m2",
		"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 "davzoku/moecule-1.1b-m2" \
        --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": "davzoku/moecule-1.1b-m2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davzoku/moecule-1.1b-m2 with Docker Model Runner:
```
docker model run hf.co/davzoku/moecule-1.1b-m2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

moecule-1.1b-m2

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using TinyLlama-1.1B-1T-OpenOrca as a base.

Models Merged

The following models were included in the merge:

tiny-llama-1.1b-chat-medical
TinyLlama-1.1B-intermediate-step-1195k-token-2.5T

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: tiny-llama-1.1b-chat-medical
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
merge_method: ties
base_model: TinyLlama-1.1B-1T-OpenOrca
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Downloads last month: 14

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for davzoku/moecule-1.1b-m2

Quantizations

1 model

Collection including davzoku/moecule-1.1b-m2

🫐♻️ Moecule Randoms

Collection

Random artifacts behind the Moecule family of models (Highly Experimental! 🚧) • 8 items • Updated Mar 19, 2025

Paper for davzoku/moecule-1.1b-m2

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19