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

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

Merged MLX release of Qwen/Qwen3.6-35B-A3B fine-tuned with a LoRA SFT run on the aisamdasu/algocean-fable5-traces trace mix.

This MLX artifact is text-generation focused. The upstream model card describes the base as a language model with a vision encoder, but the current mlx-lm Qwen3.5-MoE conversion stores the language-model weights and omits model.visual.

Use

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("aisamdasu/Algocean-Qwen3.6-35B-A3B-mlx")

messages = [{"role": "user", "content": "Create a concise LangGraph plan for a repo refactor."}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True)

Artifact

Training

Checkpoint Selection

The published weights use checkpoint 3600, not the final step 4000.

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

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 MLX inference experiments around agentic coding, tool-use traces, and LangGraph-style planning. It is not a general safety-tuned assistant release.