Instructions to use Kquant03/Samlagast-7B-laser-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/Samlagast-7B-laser-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="Kquant03/Samlagast-7B-laser-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Kquant03/Samlagast-7B-laser-bf16")
model = AutoModelForMultimodalLM.from_pretrained("Kquant03/Samlagast-7B-laser-bf16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kquant03/Samlagast-7B-laser-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/Samlagast-7B-laser-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/Samlagast-7B-laser-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kquant03/Samlagast-7B-laser-bf16

SGLang

How to use Kquant03/Samlagast-7B-laser-bf16 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 "Kquant03/Samlagast-7B-laser-bf16" \
    --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": "Kquant03/Samlagast-7B-laser-bf16",
		"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 "Kquant03/Samlagast-7B-laser-bf16" \
        --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": "Kquant03/Samlagast-7B-laser-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kquant03/Samlagast-7B-laser-bf16 with Docker Model Runner:
```
docker model run hf.co/Kquant03/Samlagast-7B-laser-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

To see what will happen. Huge thanks to NeuralNovel (Lee Jackson) for lasering it for me!!!!

Join our Discord!

GGUF FILES HERE

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

Merge Method

This model was merged using the task arithmetic merge method using paulml/NeuralOmniBeagleMBX-v3-7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: paulml/NeuralOmniWestBeaglake-7B
    parameters:
      weight: 1
  - model: FelixChao/Faraday-7B
    parameters:
      weight: 1
  - model: flemmingmiguel/MBX-7B-v3
    parameters:
      weight: 1
  - model: paulml/NeuralOmniBeagleMBX-v3-7B
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: paulml/NeuralOmniBeagleMBX-v3-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16

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Model tree for Kquant03/Samlagast-7B-laser-bf16

FelixChao/Faraday-7B

flemmingmiguel/MBX-7B-v3

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paulml/NeuralOmniWestBeaglake-7B

Merge model

this model

Quantizations

2 models

Spaces using Kquant03/Samlagast-7B-laser-bf16 9

Paper for Kquant03/Samlagast-7B-laser-bf16

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 8