Instructions to use IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep with Transformers:

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

pipe = pipeline("text-generation", model="IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep")
model = AutoModelForCausalLM.from_pretrained("IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep

SGLang

How to use IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep 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 "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep" \
    --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": "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep",
		"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 "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep" \
        --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": "IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep with Docker Model Runner:
```
docker model run hf.co/IggoOnCode/mamba-2.8b-slimpj-OpenOrca_1ep
```

mamba-2.8b-slimpj-OpenOrca_1ep / pytorch_model.bin

IggoOnCode

First version of the mamba-2.8b-slimpj-OpenOrca_1ep model and tokenizer (copy of EleutherAI/gpt-neox-20b).

b44e736 over 2 years ago

Download

History Blame Contribute Delete

5.55 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:: 1515e9a9bee143bd69582dcd12fefb34e021111a555754399d5ef2aa12056ea1
Size of remote file:: 5.55 GB
SHA256:: 630951f04627b75b525ca5fc90d189154f8d971d504cedd140c52de096cbc6c8

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