Cree1865 · Tinker RL Adapter

A reinforcement-learning adapter that read Rev. E. A. Watkins' 1865 Dictionary of the Cree Language and was scored against a deterministic, decomposed Cree reward ledger.

---

Overview

This is a GRPO reinforcement-learning adapter on Qwen/Qwen3-30B-A3B-Instruct-2507, trained for Cree (nēhiyawēwin) dictionary and orthography behavior drawn from a single historical source: Rev. E. A. Watkins' 1865 A Dictionary of the Cree Language.

It is a research bootstrap artifact built with the Dakota1890 pipeline, retargeted to Cree. Training scores each sampled answer against a deterministic Cree verifier — no LLM judge — and logs every reward channel independently so failures can be read, not guessed.

Scope, stated plainly. This is not a Cree language authority, a fluent-speaker replacement, or a production translator. It is a first, correctable model — the working endpoint for a community-in-the-loop second stage. Nēhiyawēwin belongs to its communities; this repository is a transparent technical artifact built in service of that work, not over it.

---

Model Details

Field	Value
Base model	Qwen/Qwen3-30B-A3B-Instruct-2507
Adapter	LoRA, rank 32 (PEFT)
Method	GRPO (grouped rollout RL) with a deterministic Cree reward ledger
Infrastructure	Thinking Machines Tinker
W&B run	hda2wqhl — cree1865-synthetic-expansion-v1
Steps	800
Batch / group size	16 / 8
Max sampled tokens	256
Temperature	0.9
Learning rate	4e-5
Tinker session	9d734fdb-7851-5f2f-9949-e9e574eb9a55
Languages	Cree crk, English en
Source	Watkins 1865 — Internet Archive cihm_41985
License	Apache-2.0 (code) · Public Domain (1865 text)

---

Training Data & Methodology

Everything derives from one public-domain book — a bilingual, two-part 1865 dictionary.

Part	Direction	PDF pages	State
Front matter	pronunciation key + notes	1–28	reference
Part I	English → Cree	29–210	extracted
Part II	Cree → English	212–end	extracted

Extraction snapshot (full local build, 2026-06-24):

Pages	Usable entries	Multi-variant	SFT (train/val)	RL tasks
463	19,560	4,049	18,463 / 972	38,870

Structured entries become a synthetic Q&A prompt bank and a set of verifiable RL tasks (English↔Cree translation, orthography, containment). This run trained on the balanced synthetic-expanded task set (rl_tasks_synthetic_expanded_balanced.jsonl).

---

Reward Function

A composite, deterministic reward — every channel is checkable by code, logged, and inspectable:

Channel	Weight	Verifies
Exact match	0.20	Normalized response equals the Watkins-derived answer
Target containment	0.25	Expected answer appears inside the response
Orthography recall	0.20	Cree marks, hyphens, apostrophes, and accents preserved
Character F1	0.20	Spelling-level overlap for near misses
Concise length	0.15	No padding a lookup answer with unsupported text

The verifier logs raw channel values, weighted contributions, the reconstructed composite, and a composite_diff for full auditability. This is the cree rubric — the run below was scored against exactly these channels.

---

Reward Ledger (run hda2wqhl)

Logged live to W&B for all 800 steps and pulled directly from the run.

What the curves show, honestly:

• Composite reward rises from about 0.15 to a smoothed ~0.30 band — real upward movement, though noisy.
• Concise length is the strongest channel, climbing to roughly 0.95 and holding; it is the largest weighted contribution at the end.
• Character F1 and orthography recall both contribute and move in the 0.3–0.45 range, which is where orthographic learning is visible.
• Exact match and target containment stay near 0.0. For short answer-only lookups these are the honest ceiling on composite reward and the clearest targets for the next iteration.
• Policy entropy converges, peaking early then settling to about 0.1–0.2 — the policy committing to a narrow answer style.

Composite reward, raw and smoothed.

Each Cree reward channel, tracked independently.

No fluency or community-validation claim is made from this run. The useful signals are per-channel reward movement, English→Cree versus Cree→English asymmetry, and orthography behavior on held-out prompts.

---

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Qwen/Qwen3-30B-A3B-Instruct-2507"
adapter_name    = "HarleyCooper/Cree1865"

model = AutoModelForCausalLM.from_pretrained(
    base_model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(model, adapter_name)

messages = [
    {"role": "system", "content": "You are a Cree language assistant working from Watkins 1865. Return only the answer."},
    {"role": "user",   "content": "Translate 'a good man' to Cree, preserving the 1865 orthography."},
]
text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Treat the output as a first attempt — a starting point for community correction, not a final answer.

---

Limitations & Ethical Notes

The source is a missionary-era dictionary published in 1865. It reflects the orthography, analysis, and colonial-era framing of its time, recorded across the Hudson's Bay territories. Outputs can inherit mistakes, omissions, and outdated descriptions from both the source extraction and the base model.

• This is not a Cree language authority, a fluent-speaker replacement, or a production translation system.
• Many tasks are dictionary lookups, not natural conversation; the reward verifies lookup behavior, not communicative fluency.
• Cree language work should be reviewed with appropriate community and linguistic expertise.
• The model is designed to be corrected: it is the working endpoint for a community-in-the-loop stage, not a finished teacher.
• No community has certified this model as fluent, authoritative, or safe for language instruction.

---

Citation

Watkins, E. A. (1865). A Dictionary of the Cree Language, as Spoken by the Indians of the Hudson's Bay Territories. London: Society for Promoting Christian Knowledge. Internet Archive: cihm_41985.

@misc{cree1865_model,
  title  = {Cree1865: A Single-Volume GRPO Cree Language Adapter},
  author = {Cooper, Christian Harley},
  year   = {2026},
  note   = {Base: Qwen/Qwen3-30B-A3B-Instruct-2507. Source: Watkins 1865 (IA cihm_41985).
            Method derived from Dakota1890.}
}

Infrastructure & lineage: Thinking Machines Tinker (RL), Anthropic (VLM extraction), and the Dakota1890 pipeline this work replays.