---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- text-generation
- llama
- pretraining
- from-scratch
pipeline_tag: text-generation
---

# Dot-125M-Identity

An identity-tuned variant of **[Dot-125M](https://huggingface.co/perletter/Dot-125M)**,
by **[Perletter](https://perletter.com)**, 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](https://huggingface.co/perletter/Dot-125M) 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))
```

```bash
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.