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episod
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Model Details
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episod
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Input Modalities
Output Modalities
Supported Functionality
Routing by physical die address. Tokens can be assigned to experts by where they live on the harvested 11×10 Tensix grid rather than by a learned gate, and freezing routing to that geometry costs only 0.0118 nats against a gate free to learn (|t| 5.1, 14/15 signs). Source-characteristic tokens occupy measurably distinct die regions — cell purity 0.546 against a 0.231 permutation floor. This part requires a real harvested grid to be about.
Sparse routing that pays. A Mixture of Enthusiasts beats the dense baseline from scratch, replicated at two seeds: pooled +0.0417 nats, |t| 8.0, 39/44 paired signs, separating late in training in both runs.
Thinking on demand. Asked to plan before it speaks, it emits well-formed five-slot think-blocks in 98% of generations where a control arm emits none.
| capability | state | note |
|---|---|---|
Training on Blackhole (ttml) | ✅ | one epoch ≈ 65 min, one p300c |
| Multi-chip DDP | ✅ [1,2] / ⚠️ [1,4] | the 4-chip mesh froze the host |
| Sparse MoE (Mixture of Enthusiasts) | ✅ | replicated at two seeds |
| Die-region expert routing | ✅ | 0.0118 nats to freeze to physical address |
| Thinking (five-slot think-blocks) | ✅ format / ⚠️ effect | steers measurably; does not govern |
| Per-core Gumbel sampling on device | ✅ | custom kernels, per-core RNG |
| vLLM serving (TT plugin) | ✅ | OpenAI-compatible |
tt-model packaging | ✅ v4 / ⏳ v5 | v5 needs wheels assembled |
| CPU-portable HF export | ✅ | runs without Tenstorrent hardware |
| Chat template | ✅ | ships in tokenizer_config.json; renders Q:/Answer:, caps history at 5 messages |
| Tool calling | ⚠️ separate checkpoint | these weights emit none; a continued-training run reaches 100% emission / 75% schema-valid — see below |
| Skits (multi-turn improv) | ✅ | five-turn scenes, real two-voice dialogue |
| Reach dial (controllable surprise) | ⚠️ measured, small | +0.060 residualised; plateaus; see below |
This card also records where the model fails, because a card that does not is not useful. It is not a claim that the model is good.
| parameters | 122,962,944 |
| hidden size / layers / heads | 1024 / 8 / 16 (4 KV heads) |
| context | 512 |
| vocabulary | 32,000 (BPE, trained on this corpus) |
| training | 10,764 steps, batch 64, seq 512, 4-chip DDP on one p300c |
| final validation loss | 2.8230 |
| chat history cap | last 5 messages (chat template, see above) |
4 KV heads means it shards across 1, 2 or 4 chips without violating head-divisibility.
It continues text, and — unlike earlier checkpoints in this project — it will answer a question in the shape of an answer. Under greedy decoding:
Q: What is the capital of France? → Answer: The capital of France is the city of Paris.
Q: What is the capital of Italy? → Answer: The capital of Italy is the city of Rome.
Both correct. The corpus carries wikipedia_simple at 15%, which is where facts
of that kind live.
Q: How many legs does a spider have? → Answer: The body of a spider is a frog.
Q: What color is the sky? → Answer: The sky is a blue color that is blue because it is blue because it is blue…
The second is a measured regression, not an anecdote. Against tt-tnt-1024a
(same architecture, same steps, corpus without the dialogue slice):
| signal | delta | vs seed floor | verdict |
|---|---|---|---|
| 4-gram repeat rate | +0.0074 | 3.32× | worse |
| termination rate | −0.0076 | 0.52× | not interpretable |
| genre collapse | −0.0035 | 0.06× | not interpretable |
| loss at matched window | +0.0102 | — | no floor for this instrument |
Nine of ten behavioural signals came back NOT INTERPRETABLE against this project's 0.1944-nat seed-only noise floor. The one finding that cleared both gates is that repetition got worse. A prediction that short question-answer documents would improve termination was not supported.
Full comparison:
docs/measurements/evaluation-tt-tnt-1024a-vs-tt-tnt-1024-dialogue.md.
Recorded with their limits, because all of them are easy to overstate.
Tool calling works structurally, and only structurally (2026-08-29). A continued-training
run on top of these weights teaches four tools — factual_response, witty_response,
absurdist_response, misunderstood_question — emitted as real <tool_call> blocks that
vLLM's hermes parser turns into structured tool_calls:
| gate | trained | this checkpoint (control) |
|---|---|---|
| emits a tool call | 100% | 0% |
| parses | 85.9% | 0% |
| schema-valid (registered tool, required args, legal enums) | 75.0% | 0% |
| distinct tools used | 4/4 | 0/4 |
Unseen questions score higher than seen ones (78% vs 72%), so it learned the format rather
than memorising rows, and 3/5 chat requests come back as genuine structured tool_calls
end-to-end through the server.
The content inside those calls is poor, and that is the honest headline: "The capital of
France is the capital of France"; Portugal answered "Madrid" confidently. 100 hand-written
examples in a deliberately literary register did not lift a 123M model into wit — the same
shape as this card's dialogue-slice finding, where a small curated slice bought form and not
knowledge. The tool-calling checkpoint is not published: good structure with bad content is
not a model to promote. See docs/measurements/tool-calling-stage{1,2}.json.
Two further things were measured on 2026-08-20.
Sparse routing (Mixture of Enthusiasts) beats dense from scratch. Replacing the
feed-forward with ttml's sparse MoE and training both arms one epoch from init, paired on
seed 5489: validation 2.8098 for MoE against 2.8748 for dense (mean delta +0.0481,
|t| 7.3, 20 of 22 signs), and the gap widens across training. Read it as the ordinary MoE
bargain — the configuration carries 3.62× total parameters at 0.989× active compute, so
more parameters for the same compute helped. It is not evidence about the die-region routing
below. Replicated at a second seed (8191: +0.0354, |t| 4.5, 19/22 signs; pooled +0.0417 over
44 points), with the same late-separating trajectory in both runs, so treat ~0.04 as the
estimate.
Routing by physical die address is nearly free. Tokens can be routed to experts by where they live on the harvested Tensix grid rather than by a learned gate. Freezing the gate to that geography — never letting it learn — costs only 0.0118 nats against a freely-learned gate (|t| 5.1, 14/15 signs). Seeding the gate from the die map and then letting it move buys nothing measurable (+0.0044, signs 8+/7−), even though the seeding demonstrably works as a classifier (61.2% region recovery against a 10% chance floor). The geometry is real; the loss does not care where the gate starts, only where it may end up.
A five-slot think-block can be learned, and does not yet help. Fine-tuned to emit
offer / accept / add / stakes / handback before continuing a story — one slot per improv
failure mode (escalating to the worst place, blocking with the dullest next step, drifting too
far out) — the model produces well-formed blocks in 98% of generations (784/800; the
no-think control produces them 0% of the time). Substituting another story's block changes
100% of continuations, so the block steers rather than decorates. But it moves none of
the four failure-mode scores at α = 0.01, and one of those four is saturated on the real
co-occurrence table and cannot discriminate at all. Stage 1 is partial.
The generations explain the null better than the scores do. Asked to continue a story, the
model planned add: dance / handback: dance and then wrote a scary dog; another block set
stakes: up and the scene resolved into contentment. The syntax of intention is perfect and the
intention is not honoured. Read alongside the swap test that names it precisely: the block is
context the model conditions on, not an instruction it obeys — change it and the output moves,
ask it to mean something and it shrugs. On the same opening the no-think arm writes plainly
better prose. A plainer contributing reason: the slots are telegraphese (loved play outside
friends), because derivation lifts content words and drops the rest, so the model was asked to
produce a register nothing in 400M tokens of storybook prose resembles — and then to let that
register steer one it knows fluently.
Next unit is a skit: two or more turns with a partner who answers. A single continuation
gives handback nothing to hand back to, so the slot that encodes "make your partner look good"
cannot pay off or fail. Close reading in
episod-log.md, 2026-08-21.
One process note kept deliberately: an earlier pass reported 0% adherence, from a run in which
all 17 RMSNorm gammas were provably frozen because stochastic_rounding defaults off on the SFT
path. With the gammas free, 0% became 98%. Both runs are preserved in the repo's measurement
files.
2026-08-23/24. The last experiment on this checkpoint asked whether the model's declared
plan can be turned into a control: force a reach slot to near / mid / far and see
whether the model reaches a correspondingly distant word.
It works, and it is small. Forcing the dial moves the realised semantic distance of the
model's add word monotonically — near < mid < far — scene-paired over 826 held-out
scenes, and it survives frequency control. NPMI is not frequency-neutral (rare co-occurring
pairs score high, common words are capped low), so a raw effect would partly be "far picked a
commoner word". Residualising on log document frequency:
| contrast | raw | frequency-residualised |
|---|---|---|
near < mid | +0.0839 (t 16.2) | +0.0324 (t 7.4) |
mid < far | +0.0456 (t 13.9) | +0.0281 (t 9.0) |
near < far | +0.1295 (t 23.3) | +0.0604 (t 12.5) |
About 53% of the raw effect is word frequency; ~47% survives. A control arm that never saw a
reach slot shows nothing (t 2.36 / −0.30 / 2.35, not one step significant) — and two of those
sit between stage 1's threshold and this eval's, so importing the old constant would have turned
the negative control positive.
The pre-declared EUREKA criterion was not met, on one gate: the add slot-hit rate does not
hold across settings (worst-vs-best shortfall 0.0896). The failure is a near-side dip, not the
far-side collapse the gate was written to catch — far is +0.030 above near. The gate was
in the code before the data, so it stands as written rather than being narrowed afterwards.
add slot was a discourse-particle
vocabulary (look, please, hi, hello — 18.5% of observations in the top 25). A validated
content-word filter (precision 0.949, recall 0.942) removed every particle — and the slot got
more concentrated (top-25 share 0.185 → 0.218, distinct 6,442 → 4,846) with a worse
frequency confound (spearman +0.208 → +0.233). The particle mass collapsed onto common verbs.The dial reaches for distant words. This corpus does not contain many. TinyStories is 13,777
distinct words, with the top 1,000 covering 90.9% of all tokens — dragon 959, castle 1,383,
volcano 208, comet 176, but cathedral 0, submarine 0, meteor 1, orchestra 2. far
collapsing to 88 distinct words against near's 265 is a model faithfully reflecting a world
with about a thousand usable words in it.
The mechanism is real and portable. The next investment is a corpus with range, not more training and not a better filter. One narrower experiment — a noun-preferring rank key — was started and stopped unfinished when this line of work was shelved.
Everything above re-derives from docs/measurements/reach-dial.json via
scripts/eval_reach.py --rescore-from, with no model, tokenizer, or device — verified
byte-identical, with gold distances reproducing at max absolute error 0.0.
No instruction tuning beyond a 2% slice of databricks-dolly-15k. No chat
template. No system prompt. It repeats under greedy decoding. It has 512 tokens
of context. It is a small model trained for one epoch on 352.6M tokens, and it
should be treated as an artifact of a hardware-and-tooling project rather than as
a useful assistant.
Nine sources, blended to a 400M-token budget and shipped as a recipe rather
than as text, because 46% of it is share-alike under two mutually incompatible
copyleft terms. Reconstruct it from
episod/tt-tnt-corpus.
The dialogue slice is databricks-dolly-15k (CC-BY-SA-3.0) at 2%, rendered as
plain Question: … / Answer: … prose with no role markers — the tokenizer has no
vocabulary for chat scaffolding.
Through the Tenstorrent vLLM plugin. Use a plugin at or after c127c17: earlier
builds show a decode defect that degrades free-running generation into repetition
within a few tokens. The plugin reports version 0.1.0 either way, so a version
check cannot detect this; the bundle's adapter warns structurally instead.