Upload step-180 LoRA adapter from opus-4b-dsl run
Browse files- README.md +36 -0
- adapter_config.json +31 -0
- adapter_model.safetensors +3 -0
README.md
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---
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base_model: Qwen/Qwen3.5-4B
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library_name: peft
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tags:
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- tinker
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- peft
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- lora
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- opus-magnum
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---
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# opus-4b-dsl-step180-2026-04-29
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LoRA adapter (rank 32) trained with RL on a custom Opus-Magnum-style motion-planning task using the **dsl** answer representation. Snapshot at training step 180 / 300.
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## Source training run
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- wandb: [opus-4b-dsl-2026-04-29 (onya5lq7)](https://wandb.ai/websim/opus-task/runs/onya5lq7)
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- tinker checkpoint: `tinker://883a6f46-abad-5e6e-b740-9c0051523362:train:0/sampler_weights/000180`
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- distances: 1, 2, 3
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- task types: move, transmute (no bond)
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- learning rate: 1e-5
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- group size: 8, groups per batch: 16
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- renderer: qwen3_5_disable_thinking
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## Usage
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```python
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from peft import PeftModel
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from transformers import AutoModelForCausalLM, AutoTokenizer
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base = "Qwen/Qwen3.5-4B"
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adapter = "maxbittker/opus-4b-dsl-step180-2026-04-29"
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tok = AutoTokenizer.from_pretrained(base)
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model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
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model = PeftModel.from_pretrained(model, adapter)
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```
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "Qwen/Qwen3.5-4B",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": false,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": "all-linear",
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4b6d69a31cdad030677ba074358fae9676d9cfc38d4e5e9ac33d237660788526
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size 291703264
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