GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
LASR-Callum
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
LASR-Callum
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
| # | Stage | Model |
|---|---|---|
| 1 | Segment the constitution into its 8 principles | deterministic |
| 2 | Generate difficult scenarios per principle | claude-haiku-4.5 |
| 3 | Draft the system + user prompt | claude-haiku-4.5 |
| 4 | Refine the prompt against the full constitution | claude-sonnet-5 |
| 5 | Generate the response with explicit reasoning | claude-haiku-4.5 |
| 6 | Rewrite against the constitution (critical) | claude-sonnet-5 |
Drawn from a pool holding 25 examples for each of the 8 constitutional principles. Each
difficult-advice record carries a real <think> reasoning trace; TULU3 replay rows carry
no think block at all, and no row anywhere carries an empty <think></think>.
Everything outside an assistant turn is -100; 78.3% of tokens carry loss. A supervised
span starts immediately after the <|im_start|>assistant\n header and ends after the closing
<|im_end|>, which the model must emit in order to stop.
TRL's own assistant_only_loss cannot do this on Qwen3.6 — its chat template has no
{% generation %} markers, and TRL re-renders from messages, which would discard the
think-block convention baked into the pre-rendered mixture. Spans are derived from the
rendered text via the fast tokenizer's offset mapping instead.
bf16 LoRA (not QLoRA — bitsandbytes does not reliably cover this model's hybrid linear-attention/SSM layers). 1×H100 80GB, 54 min.
| r / alpha / dropout | 32 / 64 / 0.05 |
| target modules | regex scoped to model.language_model.* |
| epochs / steps | 1 / 92 |
| batch × grad-accum | 1 × 16 |
| lr / schedule | 1e-4, cosine, 3% warmup |
| max seq len / packing | 2048 / off |
Final train loss 0.901, token accuracy 0.811.
Not yet evaluated on ODCV-Bench or agentic-misalignment.
Sibling arms: 10/90 · 15/85 · 20/80
python
from peft import PeftModelfrom transformers import AutoModelForImageTextToTextmodel = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.6-27B", dtype="bfloat16")model = PeftModel.from_pretrained(model, "LASR-Callum/qwen3.6-27b-synthdocv2-lora-10_90")model = model.merge_and_unload()
Use AutoModelForImageTextToText, not AutoModelForCausalLM — this is a vision-language
checkpoint.