Instructions to use openaccess-ai-collective/manticore-30b-chat-pyg-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openaccess-ai-collective/manticore-30b-chat-pyg-alpha with Transformers:

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

pipe = pipeline("text-generation", model="openaccess-ai-collective/manticore-30b-chat-pyg-alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openaccess-ai-collective/manticore-30b-chat-pyg-alpha")
model = AutoModelForCausalLM.from_pretrained("openaccess-ai-collective/manticore-30b-chat-pyg-alpha")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openaccess-ai-collective/manticore-30b-chat-pyg-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openaccess-ai-collective/manticore-30b-chat-pyg-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openaccess-ai-collective/manticore-30b-chat-pyg-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha

SGLang

How to use openaccess-ai-collective/manticore-30b-chat-pyg-alpha 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 "openaccess-ai-collective/manticore-30b-chat-pyg-alpha" \
    --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": "openaccess-ai-collective/manticore-30b-chat-pyg-alpha",
		"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 "openaccess-ai-collective/manticore-30b-chat-pyg-alpha" \
        --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": "openaccess-ai-collective/manticore-30b-chat-pyg-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openaccess-ai-collective/manticore-30b-chat-pyg-alpha with Docker Model Runner:
```
docker model run hf.co/openaccess-ai-collective/manticore-30b-chat-pyg-alpha
```

manticore-30b-chat-pyg-alpha / model-00007-of-00007.safetensors

winglian

epoch 0.4 before divergence of loss, safetensors

a713ab6 about 3 years ago

Download

History Blame Contribute Delete

5.69 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 16fc3e7b2f144092f9d6c05c30fb6addcc56ce04c7a1251694712f259be99dcc
Size of remote file:: 5.69 GB
SHA256:: 603ace03acb4b7f9b4cbde944a3b64f9101b4e8f741f41b28db7de8841d102f6

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.