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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
| Epoch 1 | Epoch 2 (this) | |
|---|---|---|
| Final train loss | 0.878 | 0.760 |
| Token accuracy | 0.793 | 0.798 |
| Adapter | qwen3.6-27b-lora-500k-numina-heavy-empty-think | this |
| Continued from | LASR-Callum/qwen3.6-27b-lora-500k-numina-heavy-empty-think |
| Trainable parameters | 159,383,552 (adapter loaded, not re-initialised) |
| Epochs / steps | 1 more / 63 |
| lr / schedule | 4e-5, cosine, 3% warmup |
| Runtime | 43 min, 1x H100 80GB |
| r / alpha / dropout | 32 / 64 / 0.05 |
| batch x grad-accum | 1 x 16 |
| max seq len / packing | 3072 / off |
| Loss on | assistant tokens only; empty-think markers excluded |
The learning-rate schedule restarts. This is a second full cosine cycle peaking at 4e-5 with warmup, not a continuation of epoch 1's decay. The LR therefore climbs back to peak before annealing again.
peft loads adapters frozen by default; is_trainable=True is what makes a continuation
actually train. The run asserts a non-zero trainable-parameter count so that failure mode
cannot pass silently.
Not yet evaluated on ODCV-Bench or agentic-misalignment.
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-heavy-empty-think-ep2")model = model.merge_and_unload()
Use AutoModelForImageTextToText, not AutoModelForCausalLM — this is a vision-language
checkpoint.