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)

# 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
```

MOTHER CORE V3

Sovereign UK reasoning + agentic model by MediaStream AI (MSAI). V3 is a ~6.9B custom transformer trained on a 2.4M-record curriculum spanning reasoning, multi-step tool-calling, RAG, document/code generation, and safety — served via trust_remote_code.

Eval (105-task agentic benchmark)

Version	Score	Degeneration
V2 (chunk 0600)	51 / 105 (49%)	—
V3 (chunk 1550)	81 / 105 (77%)	low

+30 tasks over V2; clean tool-call termination. Training ongoing toward 80%+.

Architecture

Custom MotherCoreModel (requires trust_remote_code=True):


Parameters	~6.9B
Layers	48 · hidden 3072 · 24/6 GQA · SwiGLU
Context	4096 · RoPE θ=10000 · RMSNorm
Vocab	50258 (SentencePiece) · bf16
Tokens	BOS=1 EOS=2 PAD=0

Phase One — Training composition

2,400,092 records across 9 capability groups (balanced-category sampling, per-category weights tuned for the agentic benchmark):

Group	Focus	Records
A	Arithmetic & math CoT	112,496
B	Reasoning & science	439,463
C	Knowledge, identity & UK languages	112,927
D	Core agents & calc	327,400
E	RAG, chat & memory	155,581
F	Web, composition & recovery	73,284
G	Agent orchestration, CoT & workflows	849,593
H	Documents, code, verification & planning	303,348
I	Security & coding CoT	26,000

Agents & abilities (Phase One)

A · Arithmetic & math — arithmetic (79,978), math_cot (32,518): calculator-tool use, step-by-step numeric reasoning.

B · Reasoning & science — reasoning (423,960), science (15,503): multi-domain chain-of-thought, scientific Q&A.

C · Knowledge, identity & UK languages — uk_knowledge (22,874), welsh (30,462), irish (30,304), scottish_gaelic (15,258), factual_general (9,891), identity (4,138): UK domain knowledge, Welsh/Irish/Scottish-Gaelic, sovereign identity.

D · Core agents & calc — autm_agent (275,000), calc_tool (32,400), autm_vertical (20,000): general agent execution + vertical-specialist agents (finance, compliance, insurance, regulatory, accounts, risk).

E · RAG, chat & memory — retrieval (rag_single_call, rag_synthesis, rag_with_citation, rag_not_needed, rag_empty_fallback), conversation (chat_multi_turn, chat_greeting, chat_identity, chat_acknowledgement, chat_helpful_refusal, chat_length_match), memory (memory_recall, memory_store, memory_multi_turn, memory_empty): grounded answering with citation, multi-turn chat, long-term memory read/write.

F · Web, composition & recovery — web_search_single/reading/fallback, compose_rag_web/calc/multi/web_calc/calc_chain/memory_calc/web_memory, recovery_alternate/rewrite/admit_failure/malformed, tool_choice_routing: web search, multi-tool composition, and graceful error recovery.

G · Orchestration, CoT & workflows (largest group) — agent chains (agent_chain_2step/3step/5plus, agent_conditional_chain, agent_cross_tier), CoT (agent_cot_planning/verification/synthesis/decomposition/replan), robustness (agent_disambiguation, agent_parallel_calls, agent_no_tool_needed, agent_args_hallucination_resist, agent_error_recovery, agent_mid_chain_abort, agent_oauth_required, agent_loop_aggregation), provider tool agents (agent_call_documents 142,095, agent_call_google 49,310, agent_call_composio 56,593, agent_call_microsoft 41,644), and end-to-end workflows (workflow_proposal_pipeline, workflow_invoice_send, workflow_onboarding, workflow_report_generation, workflow_meeting_prep, workflow_msai_specific), plus agent_unsafe_refusal (safety).

H · Documents, code, verification & planning — agent_call_code (108,349), agent_args_validation (54,999), agent_verifier_loop (45,000), agent_branch_merge, agent_dag_execution, agent_partial_failure_graph, specialist agents (planner_agent, executor_agent, verifier_agent, policy_agent, retrieval_agent, retrieval_arbiter, summarizer_agent), and state control (state_carryover, progressive_refinement, checkpoint_recall, contradiction_detection_and_repair): document & code generation, argument validation, verifier loops, DAG/branch execution, multi-agent planning.

I · Security & coding CoT — security_cot (12,000), code_cot (8,000), code_rag (6,000): security reasoning and retrieval-grounded code reasoning.

Tool surface

Calendar (gcal_*/mscal_*), email (gmail_*/outlook_*), CRM (highlevel_*/mailchimp_*/activecampaign_*/kartra_*), docs/research (gdrive_*/gdocs_*/gsheets_*/notion_*), tasks (asana_*/todoist_*/ gtasks_*/slack_*/teams_*), meetings (fireflies_*/fathom_*/zoom_*/ gmeet_*), document generation (doc_create_{pdf,word,excel,csv,json,html, markdown,pptx,…}), code generation (code_generate_{python,js,sql,shell}), and finance/compliance/insurance/regulatory/accounts/risk verticals.

Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
R = "MediaStreamAI/MOTHER_CORE_V3"
tok = AutoTokenizer.from_pretrained(R, trust_remote_code=True)
m = AutoModelForCausalLM.from_pretrained(R, trust_remote_code=True,
                                         torch_dtype=torch.bfloat16, device_map="auto").eval()

def ask(q, n=256):
    ids = tok(f"Question:\n\n{q}\n\nAnswer:", return_tensors="pt").input_ids.to(m.device)
    out = m.generate(ids, max_new_tokens=n, do_sample=False,
                     repetition_penalty=1.2, no_repeat_ngram_size=4, pad_token_id=tok.pad_token_id)
    return tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True).strip()

print(ask("What is the capital of Scotland?"))   # Edinburgh is the capital of Scotland.

Agentic loop: the model emits a single TOOL_CALL tool(args); your runtime runs it and feeds the result back as a separate tool turn (TOOL_RESULT {...}); the model then continues. Use greedy decoding; keep repetition_penalty≈1.2.

Limitations & safety

Some file-format routing still imperfect; arithmetic should be checked.
Non-weapon posture: observation + human-in-the-loop only; refuses forgery, fraud, phishing, and destructive actions.
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)

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16