item	value
Format	GGUF F16
File	`Algocean-Qwen3.6-35B-A3B.gguf`
File size	71,066,993,920 bytes
GGUF version	3
GGUF metadata keys	43
GGUF tensors	753
LoRA tensors merged	20
SHA-256	`564448e3c3861fd9f143bba4311c4a3cdc92da6709099fa25d5ed1cc0a4c3e89`

Training

key	value
Base model	`Qwen/Qwen3.6-35B-A3B`
Dataset	`aisamdasu/algocean-fable5-traces`
Train / eval rows	30,265 / 512
Selected source rows	Crownelius 26,938 + kelexine 3,839
Dedup skipped	1,721
Fine-tune method	LoRA SFT
LoRA target modules	`q_proj`, `v_proj`
LoRA rank / alpha / dropout	16 / 32 / 0.0
Max sequence length	16,384
Optimizer / schedule	AdamW fused / cosine
Learning rate / warmup	1e-5 / 0.03
Batch / grad accumulation	1 / 1
Max steps	4,000
Eval / save interval	200 / 200
GPU	Modal B200 x1
Runtime	7,934.71 sec

Checkpoint Selection

step	eval loss	eval PPL	note
200	1.300712	3.671911	first eval
3200	1.067631	2.908480	near plateau
3400	1.067996	2.909543	regression
3600	1.066911	2.906388	selected best
3800	1.067495	2.908084	worse than best
4000	1.067005	2.906661	final, slightly worse

Best checkpoint rationale:

3600 has the lowest validation loss: 1.066911.
Final step 4000 is very close but still higher: +0.000094 eval loss.
The curve flattened after 3200, so selecting 3600 keeps the best validation point without chasing extra train loss.

Metrics

metric	value
Best eval loss	1.066911
Best eval perplexity	2.906388
Final eval loss	1.067005
Final eval perplexity	2.906661
Trainer average train loss	0.494604
Last logged train loss	0.996137
Train samples/sec	0.504
Train steps/sec	0.504
Eval samples/sec at final eval	2.088

Comparison Notes

This release is the best checkpoint from the completed Algocean LoRA run. It is not claiming a public benchmark win over the base model or the upstream OptiQ model; those require the same held-out evaluation harness on all models. The selection here is based on the run's validation loss, where checkpoint 3600 is the lowest-loss point among all saved checkpoints.

Intended Use

This model is intended for local llama.cpp-compatible inference experiments around agentic coding, tool-use traces, and LangGraph-style planning. It is not a general safety-tuned assistant release.

aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Algocean-Qwen3.6-35B-A3B.gguf

Use

Artifact

Training

Checkpoint Selection

Metrics

Comparison Notes

Intended Use

aisamdasu/
Algocean-Qwen3.6-35B-A3B.gguf