README.md

---
language: [en, cy, ga, gd]
license: other
library_name: transformers
tags: [mother-core, agentic, tool-use, reasoning, rag, code, uk-sovereign]
pipeline_tag: text-generation
---

# 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

```python
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)