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Model: perletter/dot-125m-identity Source: Original Platform
2026-09-28 02:57:17 +08:00
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+---
+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.