Instructions to use ND911/Franken-Mistral-Merlinite-Maid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ND911/Franken-Mistral-Merlinite-Maid with Transformers:

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

pipe = pipeline("text-generation", model="ND911/Franken-Mistral-Merlinite-Maid")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ND911/Franken-Mistral-Merlinite-Maid")
model = AutoModelForCausalLM.from_pretrained("ND911/Franken-Mistral-Merlinite-Maid")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ND911/Franken-Mistral-Merlinite-Maid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ND911/Franken-Mistral-Merlinite-Maid"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ND911/Franken-Mistral-Merlinite-Maid",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ND911/Franken-Mistral-Merlinite-Maid

SGLang

How to use ND911/Franken-Mistral-Merlinite-Maid 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 "ND911/Franken-Mistral-Merlinite-Maid" \
    --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": "ND911/Franken-Mistral-Merlinite-Maid",
		"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 "ND911/Franken-Mistral-Merlinite-Maid" \
        --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": "ND911/Franken-Mistral-Merlinite-Maid",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ND911/Franken-Mistral-Merlinite-Maid with Docker Model Runner:
```
docker model run hf.co/ND911/Franken-Mistral-Merlinite-Maid
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Franken-Mistral-Merlinite-Maid 7B

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

Merge Details

see below

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ND911/Franken-Merlinite-Maid
        layer_range: [0, 32]
      - model: l3utterfly/mistral-7b-v0.1-layla-v4-chatml
        layer_range: [0, 32]
merge_method: slerp
base_model: ND911/Franken-Merlinite-Maid
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ND911/Franken-Mistral-Merlinite-Maid

ND911/Franken-Merlinite-Maid

l3utterfly/mistral-7b-v0.1-layla-v4-chatml

Merge model

this model

Quantizations

2 models