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ajaxdavis
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ajaxdavis
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On the unchanged 615-conversation development diagnostic, this checkpoint produced 615 nonempty, EOS-terminated responses, with 12 detected degenerate loops and a mean four-gram repeat rate of 0.0120652. In a reference-blinded GPT-5.5 review of 100 conversations, it received 15 PASS, 11 BORDERLINE, and 74 FAIL judgments. The V8 reference received 16 PASS, 9 BORDERLINE, and 75 FAIL judgments and ranked first overall. The reviewer selected no candidate as conversationally competent.
This artifact is therefore useful as a negative result: full-dialogue token supervision improved response initiation and stopping, but did not produce a reliable semantic gain and increased repetition relative to V8.
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from transformers import pipelinechat = pipeline("text-generation", model="ajaxdavis/alpha-chat-v11-m300-experimental")result = chat([{"role": "user", "content": "Hello. What are you thinking about?"}], max_new_tokens=64)print(result[0]["generated_text"][-1]["content"])
The export uses standard LlamaForCausalLM files and requires no custom model code. Its exact chat template is
included in chat_template.jinja. Do not interpret nonempty output or a low training loss as evidence of
conversational competence.
The source, experiment contract, evaluation tools, and outcome record live in the Alpha2 repository.