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LASR-Callum
Available on FriendliAI
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Model Details
Model Provider
LASR-Callum
Model Tree
Qwen/Qwen3.6-27B
Input Modalities
Output Modalities
Supported Functionality
0.538 is roughly half the loss of every mixed-corpus arm in this family (0.76-0.95), and token accuracy is ~8 points higher. That is the narrowness of the target, not model quality: NuminaMath solutions follow a highly regular mathematical structure, so next-token prediction is far easier than on open-ended instruction data. Loss converged by about step 15 of 59.
| Run | Data | Loss | Token acc |
|---|---|---|---|
| this | NuminaMath only | 0.538 | 0.878 |
| 500k maths-weighted | 67% maths + mixed | 0.878 | 0.793 |
| 500k da20 + maths | 20% difficult-advice | 0.946 | 0.776 |
Everything outside an assistant turn is -100. A supervised span starts immediately after the
<|im_start|>assistant\n header and ends after the closing <|im_end|>. Verified before
training: zero user or system tokens inside any supervised span.
loss_type: nll is set because TRL's default chunked-CE path patches the LM head and reads
forward.__func__, which fails on this checkpoint's functools.partial forward.
Not yet evaluated.
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-lora-500k-numina-only")model = model.merge_and_unload()
Use AutoModelForImageTextToText, not AutoModelForCausalLM — this is a vision-language
checkpoint.