Instructions to use therealchefdave/slumber-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use therealchefdave/slumber-7b with Transformers:

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

pipe = pipeline("text-generation", model="therealchefdave/slumber-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("therealchefdave/slumber-7b")
model = AutoModelForCausalLM.from_pretrained("therealchefdave/slumber-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use therealchefdave/slumber-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/therealchefdave/slumber-7b

SGLang

How to use therealchefdave/slumber-7b 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 "therealchefdave/slumber-7b" \
    --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": "therealchefdave/slumber-7b",
		"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 "therealchefdave/slumber-7b" \
        --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": "therealchefdave/slumber-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use therealchefdave/slumber-7b with Docker Model Runner:
```
docker model run hf.co/therealchefdave/slumber-7b
```

dave

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

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: togethercomputer/LLaMA-2-7B-32K
    parameters:
      weight: 1.0
  - model: vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy
    parameters:
      weight: 0.3
  - model: princeton-nlp/SWE-Llama-7b
    parameters:
      weight: 0.5
merge_method: linear
dtype: float16

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

F16

Model tree for therealchefdave/slumber-7b

princeton-nlp/SWE-Llama-7b

togethercomputer/LLaMA-2-7B-32K

vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy

Merge model

this model

Paper for therealchefdave/slumber-7b

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 9