Instructions to use Nitral-Archive/Laylewcules-7B-v.02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nitral-Archive/Laylewcules-7B-v.02 with Transformers:

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

pipe = pipeline("text-generation", model="Nitral-Archive/Laylewcules-7B-v.02")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nitral-Archive/Laylewcules-7B-v.02")
model = AutoModelForCausalLM.from_pretrained("Nitral-Archive/Laylewcules-7B-v.02")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nitral-Archive/Laylewcules-7B-v.02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nitral-Archive/Laylewcules-7B-v.02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nitral-Archive/Laylewcules-7B-v.02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nitral-Archive/Laylewcules-7B-v.02

SGLang

How to use Nitral-Archive/Laylewcules-7B-v.02 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 "Nitral-Archive/Laylewcules-7B-v.02" \
    --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": "Nitral-Archive/Laylewcules-7B-v.02",
		"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 "Nitral-Archive/Laylewcules-7B-v.02" \
        --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": "Nitral-Archive/Laylewcules-7B-v.02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nitral-Archive/Laylewcules-7B-v.02 with Docker Model Runner:
```
docker model run hf.co/Nitral-Archive/Laylewcules-7B-v.02
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Vision/multimodal capabilities:

If you want to use vision functionality:

You must use the latest versions of Koboldcpp.

To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo.

You can load the mmproj by using the corresponding section in the interface:

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Nitral-AI/Lewcules-7B-v.02
        layer_range: [0, 32]
      - model: l3utterfly/mistral-7b-v0.2-layla-v4
        layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Lewcules-7B-v.02
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: 13

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Nitral-Archive/Laylewcules-7B-v.02

Merges

1 model

Quantizations

2 models