Instructions to use LeroyDyer/SpydazWebAI_MiniAI_248m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/SpydazWebAI_MiniAI_248m with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/SpydazWebAI_MiniAI_248m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWebAI_MiniAI_248m")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWebAI_MiniAI_248m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LeroyDyer/SpydazWebAI_MiniAI_248m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/SpydazWebAI_MiniAI_248m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/SpydazWebAI_MiniAI_248m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LeroyDyer/SpydazWebAI_MiniAI_248m

SGLang

How to use LeroyDyer/SpydazWebAI_MiniAI_248m 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 "LeroyDyer/SpydazWebAI_MiniAI_248m" \
    --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": "LeroyDyer/SpydazWebAI_MiniAI_248m",
		"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 "LeroyDyer/SpydazWebAI_MiniAI_248m" \
        --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": "LeroyDyer/SpydazWebAI_MiniAI_248m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LeroyDyer/SpydazWebAI_MiniAI_248m with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/SpydazWebAI_MiniAI_248m
```

SpydazWebAI_MiniAI_248m / README.md

LeroyDyer

Update README.md

51f1486 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

1.1 kB

metadata

base_model:
  - Locutusque/TinyMistral-248M-v2
  - Felladrin/TinyMistral-248M-Chat-v2
library_name: transformers
tags:
  - mergekit
  - merge

merge

Finally Joined the mini AI Group!

Merge Details

Merge Method

This model was merged using the TIES merge method using Locutusque/TinyMistral-248M-v2 as a base.

Models Merged

The following models were included in the merge:

Felladrin/TinyMistral-248M-Chat-v2

Configuration

The following YAML configuration was used to produce this model:


base_model: Locutusque/TinyMistral-248M-v2
dtype: float16
merge_method: ties
parameters:
  int8_mask: 1.0
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 12]
    model: Felladrin/TinyMistral-248M-Chat-v2
    parameters:
      density: [1.0, 0.7, 0.1]
      weight: 1.0
  - layer_range: [0, 12]
    model: Locutusque/TinyMistral-248M-v2
    parameters:
      density: 0.5
      weight: [0.0, 0.3, 0.7, 1.0]