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naderu-loom-7b v0.1.0 — QLoRA(Mistral-7B-v0.3), eval PASS

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LICENSE ADDED
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+ naderu-loom-7b — License
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+
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+ Copyright © 2026 BytesBrains Pte. Ltd. (Naderu)
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+
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+ This model is released under the Apache License, Version 2.0.
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+
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+ It is a derivative (LoRA fine-tune, merged) of mistralai/Mistral-7B-Instruct-v0.3,
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+ which is licensed under Apache-2.0. The upstream license and terms carry through to
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+ this derivative. A copy of the Apache-2.0 license is available at:
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+
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+ https://www.apache.org/licenses/LICENSE-2.0
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+
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+ The Naderu-authored training data and fine-tuning code in this repository are likewise
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+ provided under Apache-2.0.
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: mistralai/Mistral-7B-Instruct-v0.3
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ language:
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+ - en
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+ tags:
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+ - interactive-fiction
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+ - game-master
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+ - json
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+ - structured-output
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+ - lora
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+ - qlora
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+ - mistral
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+ - naderu
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+ ---
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+
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+ # naderu-loom-7b
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+
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+ **An offline interactive-fiction narrator by [Naderu](https://naderu.com) — a BytesBrains Pte. Ltd. venture.**
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+
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+ `naderu-loom` is a compact, specialised **game-master** model. Given a game **state** and the
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+ player's **action**, it narrates the next scene and returns a single **JSON turn** an app can
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+ render and apply — built to run **offline on-device**. It is an honest portfolio/demonstration
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+ piece: a small creative model with a **quantitative evaluation gate**, not a reasoning engine.
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+
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+ ## Provenance
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+
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+ - **Fine-tuned from:** [`mistralai/Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) (Apache-2.0).
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+ - **Method:** LoRA (r=16, α=32) trained as **QLoRA on the 4-bit MLX quant** of the base
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+ ([`mlx-community/Mistral-7B-Instruct-v0.3-4bit`](https://huggingface.co/mlx-community/Mistral-7B-Instruct-v0.3-4bit)),
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+ LoRA on 16 layers, lr 3e-5, 1000 iters, assistant-tokens-only (`--mask-prompt`), seq 1024.
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+ Trained with **MLX** on an Apple M4 Mac Mini (24 GB, no GPU); ~6.5 GB peak, final val loss 0.23.
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+ These weights are the adapter **fused and de-quantized to bf16** for portability.
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+ - **Training data:** ~320 Naderu-authored `(state, action) → JSON turn` examples across five
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+ genres (fantasy, mystery, sci-fi, horror, fairytale). License-clean and reproducible.
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+ - **License:** Apache-2.0 (inherits the base model's terms).
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+
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+ ## The turn contract
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+
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+ **Mistral-7B-v0.3 has no `system` role**, so the contract is folded into the user message. Each
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+ turn the model receives `STATE` (genre, tone, hp, inventory, flags) + an `ACTION`, and replies
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+ with exactly one JSON object:
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+
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+ ```json
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+ {
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+ "scene": "2–4 sentences of narration.",
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+ "choices": ["2 to 4 short action strings"],
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+ "state_delta": {"inventory_add": ["rusty key"], "inventory_remove": [], "flags_set": {"door_unlocked": true}, "hp": 0}
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+ }
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+ ```
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+
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+ Rules: JSON only; 2–4 choices; never remove/use an item the player does not hold; flags stay
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+ consistent with the story; honor genre and tone. The app applies `state_delta` and renders
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+ `choices` as buttons.
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+
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+ ## How to use
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+
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+ ```python
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+ import json
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ SYSTEM = (
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+ "You are naderu-loom, an offline interactive-fiction narrator (a game master) by "
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+ "Naderu (naderu.com). Each turn you receive the game STATE and the player's ACTION, "
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+ "and you reply with exactly one JSON object and nothing else, matching this schema: "
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+ '{"scene": <2-4 sentence narration>, "choices": [<2 to 4 short action strings>], '
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+ '"state_delta": {"inventory_add": [..], "inventory_remove": [..], '
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+ '"flags_set": {..}, "hp": <integer change, 0 if none>}}. '
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+ "Rules: reply with JSON only; never remove or use an item the player does not have; "
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+ "keep flags consistent with the story so far; honor the genre and tone; keep choices "
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+ "between 2 and 4."
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+ )
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+
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+ mid = "bytesbrains/naderu-loom-7b"
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+ tok = AutoTokenizer.from_pretrained(mid)
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+ model = AutoModelForCausalLM.from_pretrained(mid)
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+
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+ state = {"genre": "fantasy", "tone": "grim", "hp": 10, "inventory": [], "flags": {}}
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+ user = f"{SYSTEM}\n\nSTATE: {json.dumps(state)}\nACTION: __start__" # no system role — fold it in
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+ enc = tok.apply_chat_template([{"role": "user", "content": user}],
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+ add_generation_prompt=True, return_tensors="pt")
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+ out = model.generate(enc, max_new_tokens=256, do_sample=False)
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+ print(tok.decode(out[0, enc.shape[1]:], skip_special_tokens=True))
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+ ```
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+
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+ Tip: clamp the returned `choices` list to ≤ 4 as a thin output-validation layer (see the eval note).
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+
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+ ## Evaluation
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+
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+ **Suite:** `eval/suites/naderu-loom/run_eval.py` (v1, greedy) · **Run:** 2026-07-15
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+ (34 turns / 9 scripted playthroughs, 5 genres) · **Result: PASS**
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+
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+ | Metric | Gate | Result |
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+ |--------|------|--------|
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+ | valid_json_rate | ≥ 0.98 | **1.000** ✅ |
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+ | schema_rate | ≥ 0.95 | **0.971** ✅ (33/34) |
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+ | state_violations | 0 | **0** ✅ |
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+ | constraint_rate | ≥ 0.98 | **1.000** ✅ |
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+
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+ Honest note: **1 of 34 turns emitted 5 choices** (over the 2–4 bound), reflected in
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+ `schema_rate` (0.971), which still clears its 0.95 bar. Clamp choices to ≤ 4 in the app.
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+ World-state tracking holds — the model correctly refuses to use an item it doesn't hold
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+ (0 state violations). Narrative quality is coherent and on-tone across genres but is
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+ **reported, not gated** (subjective).
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+
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+ ## Limitations & risks
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+
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+ - 7B → limited long-horizon reasoning; **the app is the source of truth for state** (the model
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+ proposes `state_delta`, the app applies and validates it).
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+ - Fiction may be clichéd, repetitive, or tonally off; English-first.
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+ - Trained via 4-bit QLoRA, so it carries the base's 4-bit quantization characteristics.
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+ - Model-generated fiction — add content controls for a general audience.
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+
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+ ## About Naderu
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+
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+ [**Naderu**](https://naderu.com) is an AI-models company (a BytesBrains Pte. Ltd. venture). We
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+ **train** foundation models into specialised ones, **release** them with model cards, provenance,
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+ and clear licensing, and **serve** the engineering around them. Every capability claim is backed
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+ by a real evaluation — never vibes. Recipe, dataset, and eval gate are open in the Naderu repo.
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+
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+ - 🌐 [naderu.com](https://naderu.com) · 🧱 Base [`Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) (Apache-2.0) · 🏷️ v0.1.0 (2026-07-15)
additional_chat_templates/tool_use.jinja ADDED
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+ {{bos_token}}{% set user_messages = messages | selectattr('role', 'equalto', 'user') | list %}{% for message in messages %}{% if message['role'] == 'user' %}{% if message == user_messages[-1] %}{% if tools %}{{'[AVAILABLE_TOOLS]'+ tools|string + '[/AVAILABLE_TOOLS]'}}{% endif %}{{ '[INST]' + message['content'] + '[/INST]' }}{% else %}{{ '[INST]' + message['content'] + '[/INST]' }}{% endif %}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + ' ' + eos_token}}{% elif message['role'] == 'tool_results' %}{{'[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]'}}{% elif message['role'] == 'tool_calls' %}{{'[TOOL_CALLS]' + message['content']|string + eos_token}}{% endif %}{% endfor %}
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+ {{bos_token}}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ ' [INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + ' ' + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}
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