Instructions to use rAIfle/Sloppier-Wingman-Alternative-8x7B-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rAIfle/Sloppier-Wingman-Alternative-8x7B-hf with Transformers:

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

pipe = pipeline("text-generation", model="rAIfle/Sloppier-Wingman-Alternative-8x7B-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rAIfle/Sloppier-Wingman-Alternative-8x7B-hf")
model = AutoModelForCausalLM.from_pretrained("rAIfle/Sloppier-Wingman-Alternative-8x7B-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rAIfle/Sloppier-Wingman-Alternative-8x7B-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rAIfle/Sloppier-Wingman-Alternative-8x7B-hf

SGLang

How to use rAIfle/Sloppier-Wingman-Alternative-8x7B-hf 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 "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf" \
    --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": "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf",
		"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 "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf" \
        --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": "rAIfle/Sloppier-Wingman-Alternative-8x7B-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rAIfle/Sloppier-Wingman-Alternative-8x7B-hf with Docker Model Runner:
```
docker model run hf.co/rAIfle/Sloppier-Wingman-Alternative-8x7B-hf
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Sloppier-Wingman-Alternative-8x7B-hf

Alternative to rAIfle/Sloppy-Wingman-8x7B-hf. Second part of the merge has a bit of difference compared to the other one. I, personally, still prefer ChatML on this one, but Alpaca and/or Mistral-formats ought to work regardless.

models:
  - model: mistralai/Mixtral-8x7B-v0.1+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs
    parameters:
      weight: 0.33
  - model: mistralai/Mixtral-8x7B-v0.1+wandb/Mixtral-8x7b-Remixtral
    parameters:
      weight: 0.33
merge_method: task_arithmetic
base_model: mistralai/Mixtral-8x7B-v0.1
dtype: float16

and

models:
  - model: mistralai/Mixtral-8x7B-Instruct-v0.1+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b
    parameters:
      weight: 0.85
  - model: mistralai/Mixtral-8x7B-Instruct-v0.1+SeanWu25/Mixtral_8x7b_Medicine
    parameters:
      weight: 0.33
  - model: notstoic/Nous-Hermes-2-Mixtruct-v0.1-8x7B-DPO-DARE_TIES
    parameters:
      weight: 0.25
merge_method: task_arithmetic
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
dtype: float16

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

./02.5-pal-instruct
./01-pal-base

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ./01-pal-base
  - model: ./02.5-pal-instruct
merge_method: slerp
base_model: ./01-pal-base
parameters:
  t:
    - value: 0.66
dtype: float16

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Safetensors

Model size

47B params

Tensor type

F16

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