Instructions to use Nethermind/Mpt-Instruct-DotNet-XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nethermind/Mpt-Instruct-DotNet-XS with Transformers:

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

pipe = pipeline("text-generation", model="Nethermind/Mpt-Instruct-DotNet-XS", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Nethermind/Mpt-Instruct-DotNet-XS", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nethermind/Mpt-Instruct-DotNet-XS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nethermind/Mpt-Instruct-DotNet-XS

SGLang

How to use Nethermind/Mpt-Instruct-DotNet-XS 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 "Nethermind/Mpt-Instruct-DotNet-XS" \
    --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": "Nethermind/Mpt-Instruct-DotNet-XS",
		"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 "Nethermind/Mpt-Instruct-DotNet-XS" \
        --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": "Nethermind/Mpt-Instruct-DotNet-XS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nethermind/Mpt-Instruct-DotNet-XS with Docker Model Runner:
```
docker model run hf.co/Nethermind/Mpt-Instruct-DotNet-XS
```

Mpt-Instruct-DotNet-XS

Commit History

Jun 4, 2025

01:22
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
064a5d2

Sep 11, 2023

06:33
GGML models that can run f16 41.68 ms per token and q8 23.76 ms per token giving good results

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56d7c99

Sep 9, 2023

19:49
Usage example

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59929e0
19:07
Trained model

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5df4390
17:03
Update README.md

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730e4ac
17:00
initial commit

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eaf1cef

Commit History

Jun 4, 2025

Adding `safetensors` variant of this model⁠

Sep 11, 2023

GGML models that can run f16 41.68 ms per token and q8 23.76 ms per token giving good results

Sep 9, 2023

Usage example

Trained model

Update README.md

initial commit