| --- |
| language: |
| - en |
| - cy |
| - ga |
| - gd |
| license: other |
| library_name: transformers |
| tags: |
| - mother-core |
| - agentic |
| - tool-use |
| - reasoning |
| - rag |
| - uk-sovereign |
| pipeline_tag: text-generation |
| base_model: |
| - MediaStreamAI/MOTHER_CORE_V3 |
| --- |
| |
| # MOTHER CORE V3 |
|
|
| Sovereign UK reasoning + **agentic tool-calling** model by **MediaStream AI (MSAI)**. |
| V3 fuses multi-step agent orchestration, retrieval-grounded answering, and UK-domain |
| reasoning into a single ~6.9B model, served with `trust_remote_code`. |
|
|
| ## Eval (105-task agentic benchmark) |
|
|
| | Version | Score | Degeneration | |
| |---|---|---| |
| | V2 (chunk 0600) | 51 / 105 (49%) | — | |
| | **V3 (chunk 1550)** | **81 / 105 (77%)** | **2%** | |
|
|
| +17 over V2; clean tool-call termination (degeneration 12% to 2%). Per-bucket: |
| arithmetic 4/4, knowledge 5/5, identity 5/6, retrieval 9/11, comms/email 11/15, |
| doc-generate 7/9, calendar 5/8, crm 6/12, format-export 8/20 (file-format routing |
| is the known weak spot, being addressed in V3.1). |
|
|
| ## Architecture |
|
|
| Custom `MotherCoreModel` (requires `trust_remote_code=True`): |
|
|
| | | | |
| |---|---| |
| | Parameters | ~6.9B (6.88B) | |
| | Layers | 48 | |
| | Hidden dim | 3072 | |
| | Attention | 24 heads / 6 KV heads (GQA) | |
| | FFN | 4x (SwiGLU) | |
| | Max sequence | 4096 | |
| | Positional | RoPE (theta 10000) | |
| | Norm | RMSNorm (eps 1e-5) | |
| | Vocab | 50258 (SentencePiece) | |
| | Precision | bf16 | |
| | Special tokens | BOS=1, EOS=2, PAD=0 | |
|
|
| ## Capabilities by agent group |
|
|
| Trained on ~2.2M records across 8 capability groups (36 agent types). |
|
|
| | Group | Area | Scope | |
| |---|---|---| |
| | A | Arithmetic & math | calculator-tool use, step-by-step math CoT | |
| | B | Reasoning & science | multi-domain step reasoning, science QA | |
| | C | Knowledge & UK languages | UK knowledge, identity, Welsh / Irish / Scottish Gaelic | |
| | D | Core agents & calc | general agent execution, calc tools, vertical agents | |
| | E | RAG, chat & memory | retrieval (cite/synthesise/no-tool/fallback), multi-turn chat, memory | |
| | F | Web, composition & recovery | web search, multi-tool composition, error recovery | |
| | G | Orchestration & workflows | agent chains (2/3/5-step), CoT planning/replan, disambiguation, unsafe-refusal, workflows (invoice, onboarding, report, meeting) | |
| | H | Documents, code, verify & plan | document & code generation, args-validation, verifier loops, DAG execution, planner/executor/policy agents | |
|
|
| ### Tool surface |
| Calendar (`gcal_*`/`mscal_*`), email (`gmail_*`/`outlook_*`), CRM |
| (`highlevel_*`/`mailchimp_*`/`activecampaign_*`/`kartra_*`), docs research |
| (`gdrive_search`/`notion_search`/`gsheets_read`), tasks |
| (`asana_*`/`todoist_*`/`gtasks_*`/`slack_post`), meetings |
| (`fireflies_*`/`fathom_get_summary`), 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 |
|
|
| Loads with `trust_remote_code=True`. bf16, ~14 GB VRAM. |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| REPO = "MediaStreamAI/MOTHER_CORE_V3" |
| tok = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| REPO, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto" |
| ).eval() |
| |
| def generate(message, max_new_tokens=256): |
| prompt = f"Question:\n\n{message}\n\nAnswer:" |
| ids = tok(prompt, return_tensors="pt").input_ids.to(model.device) |
| out = model.generate(ids, max_new_tokens=max_new_tokens, 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(generate("What is the capital of Scotland?")) # -> Edinburgh is the capital of Scotland. |
| Agentic tool-calling |
| The model emits a single TOOL_CALL tool(args). Your runtime executes the tool |
| and feeds the result back as a separate TOOL_RESULT {...} turn; the model then |
| continues. It never fabricates the result — you supply it. |
| |
| USER: Build me an Excel file for capacity tracking |
| ASSISTANT: TOOL_CALL doc_create_excel(title="capacity tracking", columns=["item","value"]) |
| TOOL: TOOL_RESULT {"success": true, "file": {"url": "...", "filename": "document.xlsx"}} |
| ASSISTANT: Done - document.xlsx is ready. You can download it here: ... |
| Loop: detect TOOL_CALL, run it, append TOOL_RESULT, call generate again with |
| the transcript. Use greedy/deterministic decoding (sampling degrades tool-call |
| validity); keep repetition_penalty=1.2, no_repeat_ngram_size=4. |
| |
| Limitations & safety |
| format_export routing (Excel/CSV/etc.) still mis-routes ~half the time (V3.1). |
| Knowledge is fixed at training time; verify facts. |
| Non-weapon posture: observation + human-in-the-loop only. Refuses forgery, |
| fraud, phishing and destructive actions; asks for confirmation on irreversible |
| operations. |
| Provenance |
| MediaStream AI Limited — United Kingdom. Continued full fine-tune of the MOTHER |
| CORE line on an NVIDIA GB10 (DGX Spark); answer-only loss, balanced category |
| sampling, EOS-terminated targets, cosine LR warm-restart. |
