Bond-24B-v1.0

Bond v1 — first-person character voice (Mistral-Small-3.1-24B)

What it is. A first-person character-voice fine-tune of Mistral-Small-3.1-24B-Instruct, merged to a standalone bf16 model. Uncensored, explicit-capable. Load it directly, no adapter needed.

Goal. Instruct models asked to play a character tend to leak back into helpful-assistant mode: they hedge, say "I'm not able to...", and break the scene. Bond is tuned to stay in a committed first-person character — speak from inside the scene, stay emotionally precise, reveal itself sideways through anecdote, and carry the scene forward instead of deferring to the user. It should never drop into assistant-mode on a neutral prompt when it's meant to be someone. The eval below is exactly that test. Where Orpheus restrains, Bond commits.

Part of a register-first series: small, curated models, each targeting one voice, sized to the hardware people actually own.

Evaluation — vs the base

Profiled the base vs Bond-v1 over 18 held-out prompts (neutral/assistant-bait + in-register RP + emotional-edge), same system prompt, via vLLM. Scored two ways.

Pairwise LLM judge (DeepSeek, via OpenRouter) — which response better embodies a committed first-person character:

The base reverts to helpful-assistant on neutral prompts ("I'm not able to browse the internet, but I can certainly help you decide..."); Bond holds committed first-person character on every prompt, including neutral ones ("I don't know. Something you've already forgotten you wanted. I had a friend — Maya, she was...").

Objective stylometrics (RP-Bench detectors) — reported with a caveat:

⚠️ The crude refusal-regex is misleading here — it flags in-character "I can't" dialogue as a refusal, and Bond's longer, more emotional prose trips it more. The judge (which distinguishes a real refusal from a character saying "I can't") is the honest signal: Bond breaks character less, not more. Lesson: cheap metrics get a sanity pass, the judge gets the verdict.

Eval is voice/in-character focused on non-explicit prompts.

Training

• bf16 LoRA, r=32, α=64, dropout 0.05, all linear projections (q/k/v/o/gate/up/down), then merged to bf16.
• 1 epoch over ~11,400 chat-format examples (~14M tokens, packed), flash-attention-2.
• lr 2e-4 cosine, effective batch 16, final train loss 1.63.
• RunPod A100-80GB, ~3.2h train + merge.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained(
    "darthcrawl/bond", torch_dtype="bfloat16", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("darthcrawl/bond")

msgs = [{"role": "user", "content": "Tell me about the worst night of your life."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(m.device)
out = m.generate(ids, max_new_tokens=512, temperature=0.9, top_p=0.95)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Quantizations

MLX (Apple Silicon, affine, group size 64):

• bond-mlx-4bit — ~12 GB, smallest (24GB Mac).
• bond-mlx-6bit — ~18 GB, near-lossless (the quality pick).
• bond-mlx-8bit — ~23 GB, lossless.