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)

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

Architecture

Custom MotherCoreModel (requires trust_remote_code=True):

Phase One — Training composition

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

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)