Instructions to use MediaStreamAI/MOTHER_CORE_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MediaStreamAI/MOTHER_CORE_V2 with Transformers:

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

pipe = pipeline("text-generation", model="MediaStreamAI/MOTHER_CORE_V2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MediaStreamAI/MOTHER_CORE_V2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MediaStreamAI/MOTHER_CORE_V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MediaStreamAI/MOTHER_CORE_V2"
# 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_V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MediaStreamAI/MOTHER_CORE_V2

SGLang

How to use MediaStreamAI/MOTHER_CORE_V2 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_V2" \
    --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_V2",
		"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_V2" \
        --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_V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MediaStreamAI/MOTHER_CORE_V2 with Docker Model Runner:
```
docker model run hf.co/MediaStreamAI/MOTHER_CORE_V2
```

MOTHER CORE V2 — chunk 600 (W2.8 cutover base)

Sovereign UK AI built from scratch by MediaStream AI Limited (MSAI).

This is MOTHER CORE BASE — the frozen foundation checkpoint at chunk 600 of the W2.7 → W2.8 training programme. All downstream MOTHER models (DEFENCE, ROBOTICS, LLM, CODE) build on this base.

Founder & CEO and Lead AI Architect: Christopher Kenna
Parameters: 6.88B (FP32 source, BF16 weights here)
Architecture: 48 layers, dim 3072, 24 heads, 6 KV heads (GQA 4:1), RoPE θ=10000, RMS norm, tied embeddings
Context: 4096 tokens
Training: From-scratch sovereign UK build — no fine-tuning of external models
Source SHA256: 0b1ef35ec60af4a7ad0648498de8526cb775a19501dda94dfbda1713e0475b60

Training journey

Milestone	Eval (105-question harness)
Chunk 450 (initial W2.7 baseline)	47/105 (45%)
Chunk 506 (post LR-fix rollback)	44/105 (42%)
Chunk 550 (recovery, LR-capped)	46/105 (44%)
Chunk 600 (BASE freeze)	49/105 (47%)

Scope

MOTHER CORE handles: math, science, reasoning, chain-of-thought, UK knowledge, MOTHER identity, tool calling (agents, RAG, memory, workflows), multilingual responses (English, Welsh, Irish, Scottish Gaelic), safety refusals.

MOTHER CORE does NOT handle (separate sister models):

MOTHER CODE — software engineering, code generation
MOTHER LLM — long-form creative writing, instruction-tuned content
MOTHER DEFENCE — defence reasoning and strategy (W3 programme, builds on this BASE)
MOTHER ROBOTICS — humanoid robot embodiment (W4 programme, builds on this BASE)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tok = AutoTokenizer.from_pretrained("MediaStreamAI/MOTHER_CORE_V2")
model = AutoModelForCausalLM.from_pretrained(
    "MediaStreamAI/MOTHER_CORE_V2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Question:\n\nWhat is the capital of Wales?\n\nAnswer:"
inputs = tok(prompt, return_tensors="pt", add_special_tokens=True).to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=200,
    do_sample=False,
    repetition_penalty=1.3,
    no_repeat_ngram_size=4,
    pad_token_id=tok.pad_token_id,
)
print(tok.decode(out[0], skip_special_tokens=True))

Critical inference rules:

Prompt wrap: "Question:\n\n{q}\n\nAnswer:" (exact whitespace)
BOS token: 1 (required, add_bos_token=True)
EOS token: 2
PAD token: 0
Use greedy decoding only. Sampling produces gibberish.
Repetition penalty: 1.3, frequency-scaled
No-repeat n-gram size: 4

Programme context

W2.7 (complete) — Core capability training: math, science, reasoning, identity, UK knowledge, multilingual, agent tool-calling, RAG, chat, memory, workflows
W2.8 (in progress) — Document routing, argument validation, agent verifier loops, multi-step orchestration
W3 — MOTHER DEFENCE (defence reasoning and strategy)
W4 — MOTHER ROBOTICS (embodied awareness for humanoid platforms)

UK sovereign infrastructure: Manchester (HQ), Dundee (flagship DC), Durham. Phase 2 expansion H2 2026 to Düsseldorf, South Africa, Jamaica.

License

MSAI Sovereign License. See LICENSE file. Built sovereign in the UK, not derived from any externally-licensed pre-trained model.

Contact

MediaStream AI Limited West Tower, 371 Deansgate, Manchester M15 4UR, United Kingdom

mediastreamai.com

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Safetensors

Model size

7B params

Tensor type

BF16