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perletter/
dot-125m-identity

Name: dot-125m-identity
Availability: InStock

text-generation-inference

endpoints_compatible

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Dot-125M-Identity

An identity-tuned variant of Dot-125M, by Perletter, part of Chirping Waves Limited (Ireland). Same base architecture and pretrained weights, with a short additional fine-tuning pass so the model can name itself and its creator when asked, in a Q: ...\nA: ... prompt format.

Everything about the base model applies here too — see the Dot-125M model card for full pretraining details, benchmarks, and limitations. This card only covers what's different.

What was added

A small supervised fine-tune (~300 packed 512-token blocks: ~150 varied-phrasing identity Q&A pairs + ~150 unrelated plain-text examples mixed in to guard against the model overfitting into injecting its identity into unrelated answers) on top of ckpt_best.pt, 15 epochs, low learning rate (2e-5), loss masked to the answer span only.

How reliable is it?

Honestly: partially, not perfectly. Tested on phrasings not seen during fine-tuning:

Direct/simple phrasings ("What is your name?", "Who made you?") — reliable.
Rephrased/indirect phrasings ("Who exactly are you?", "I'm curious, who made you?") — correct on most tries, wrong or missing on some (sampling-dependent — this is a 133.7M model, not a large instruction-tuned one).
Non-identity questions — verified no bleed-through in direct testing (the model doesn't start injecting "I'm Dot-125M" into unrelated answers), which was the main risk this fine-tune's mixed-data design was meant to prevent.
One inconsistency worth disclosing plainly: a quick GGUF (llama-cli) spot-check using different default sampling settings than the direct-PyTorch tests failed to recall the identity fact on one sample. The safetensors and GGUF weights are verified byte-parity/near-parity identical (0.0 logit diff on export, <0.2% bpb quantization delta) — this looks like ordinary sampling variance on an already-imperfect (not 100%) fine-tune, not a quantization bug, but it means: don't expect this to work every single time. Use lower temperature / greedy decoding for more consistent recall if that matters for your use case.

If you need this to be fully reliable rather than "mostly," that needs a larger/longer SFT pass than this one, which was intentionally kept small and disclosed as such.

How to use

Same as the base model — swap the repo name:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("perletter/Dot-125M-Identity")
model = AutoModelForCausalLM.from_pretrained("perletter/Dot-125M-Identity")

prompt = "Q: What is your name?\nA:"
inputs = tok(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=30)
print(tok.decode(out[0], skip_special_tokens=True))

llama-cli -m model-Q8_0.gguf -p "Q: What is your name?\nA:" -n 30

License

Apache 2.0, same as the base model — see LICENSE. Training code not included.

perletter/dot-125m-identity