Instructions to use MediaStreamAI/MOTHER_CORE_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MediaStreamAI/MOTHER_CORE_V3 with Transformers:

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

pipe = pipeline("text-generation", model="MediaStreamAI/MOTHER_CORE_V3", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MediaStreamAI/MOTHER_CORE_V3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MediaStreamAI/MOTHER_CORE_V3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MediaStreamAI/MOTHER_CORE_V3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MediaStreamAI/MOTHER_CORE_V3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MediaStreamAI/MOTHER_CORE_V3

SGLang

How to use MediaStreamAI/MOTHER_CORE_V3 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 "MediaStreamAI/MOTHER_CORE_V3" \
    --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": "MediaStreamAI/MOTHER_CORE_V3",
		"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 "MediaStreamAI/MOTHER_CORE_V3" \
        --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": "MediaStreamAI/MOTHER_CORE_V3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MediaStreamAI/MOTHER_CORE_V3 with Docker Model Runner:
```
docker model run hf.co/MediaStreamAI/MOTHER_CORE_V3
```

MediaStreamAI commited on Jun 16

Commit

5581920

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1 Parent(s): c4f8a13

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@@ -146,6 +146,6 @@ model then continues. Use greedy decoding; keep `repetition_penalty≈1.2`.
 - Knowledge fixed at training time — verify before acting.
 ## Provenance
-MediaStream AI Limited (UK). Full fine-tune on an NVIDIA GB10 (DGX Spark);
 answer-only loss, balanced-category sampling, EOS-terminated targets, cosine-LR
-warm-restart. "Phase One" = the 2.4M-record agentic curriculum above.

 - Knowledge fixed at training time — verify before acting.
 ## Provenance
+MediaStream AI Limited (UK). Full training;
 answer-only loss, balanced-category sampling, EOS-terminated targets, cosine-LR
+warm-restart. "Phase One" = the 2.4M-record agentic curriculum above.  Phase 2  15m Records abd Coding + World Model (Backbone Deployment: MOTHER EXO World Model V.1)