{"repo": "answers111/jlens", "files": {"fits.py": {"4e6991b906024cf0357d61ba422323c4e3b9cfbb": {"t": 1785603881.189666, "n": 398, "h": "f44231aecf93a6f0", "c": "import transformers, jlens\n\nhf = transformers.AutoModelForCausalLM.from_pretrained(\"/opt/tiger/neuronpedia/jacobian-lens/hf_ckpt\").cuda()\ntok = transformers.AutoTokenizer.from_pretrained(\"/opt/tiger/neuronpedia/jacobian-lens/hf_ckpt\")\nmodel = jlens.from_hf(hf, tok)\nmy_prompts=\"介绍一下merlin\"\nlens = jlens.fit(model, prompts=my_prompts, checkpoint_path=\"out/ckpt.pt\")\nlens.save(\"out/jacobian_lens.pt\")"}}, "pyproject.toml": {"facb1859429522ce7a695a3a65970101cbdae4cb": {"t": 1785603882.3583128, "n": 847, "h": "658bfbe1d7741033", "c": "[build-system]\nrequires = [\"setuptools>=64\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"jlens\"\nversion = \"0.1.0\"\ndescription = \"Companion code for 'Verbalizable Representations Form a Global Workspace in Language Models'\"\nreadme = \"README.md\"\nrequires-python = \">=3.10\"\nlicense = \"Apache-2.0\"\ndependencies = [\n    \"torch\",\n    \"huggingface_hub\",\n    \"transformers>=5.5\",\n    \"numpy\",\n]\n\n[project.urls]\nRepository = \"https://github.com/anthropics/jacobian-lens\"\nPaper = \"https://transformer-circuits.pub/2026/workspace/index.html\"\n\n[project.optional-dependencies]\ndev = [\"pytest\", \"ruff\", \"datasets\"]\n\n[tool.ruff]\nline-length = 88\ntarget-version = \"py310\"\n\n[tool.ruff.lint]\nselect = [\"E\", \"F\", \"W\", \"I\", \"B\", \"UP\"]\nignore = [\"E501\", \"E741\"]\n\n[tool.setuptools]\npackages = [\"jlens\"]\n\n[tool.setuptools.package-data]\njlens = [\"data/*\"]\n"}}, "run.py": {"ba35e30868629dc28c2d8045f56172afbbfa3dc2": {"t": 1785603883.5563865, "n": 1514, "h": "a3395b6740625282", "c": "import os\n\nimport torch\nimport transformers\n\nimport jlens\n\n\nMODEL_PATH = os.environ.get(\"JLENS_MODEL_PATH\", \"/opt/tiger/neuronpedia/hf_ckpt\")\nMAX_MEMORY = {i: \"72GiB\" for i in range(torch.cuda.device_count())}\n\nlens = jlens.JacobianLens.from_pretrained(\"stanleytheli/qwen3.6-35B-A3B-jlens\")\n\nhf = transformers.AutoModelForCausalLM.from_pretrained(\n    MODEL_PATH,\n    dtype=torch.bfloat16,\n    device_map=\"balanced\",\n    max_memory=MAX_MEMORY,\n    low_cpu_mem_usage=True,\n)\nprint(\"HF device map:\", getattr(hf, \"hf_device_map\", None))\ntok = transformers.AutoTokenizer.from_pretrained(MODEL_PATH)\nmodel = jlens.from_hf(hf, tok)\n\nlens_logits, model_logits, _ = lens.apply(\n    model, \"Fact: The currency used in the country shaped like a boot is\",\n    positions=[-2])\nfor layer, logits in sorted(lens_logits.items()):\n    print(layer, [tok.decode([t]) for t in logits[0].topk(5).indices])\n\n# import transformers, jlens\n\n# lens = jlens.JacobianLens.from_pretrained(\"stanleytheli/qwen3.6-35B-A3B-jlens\")\n\n# hf = transformers.AutoModelForCausalLM.from_pretrained(\"/opt/tiger/neuronpedia/jacobian-lens/hf_ckpt\").cuda()\n# tok = transformers.AutoTokenizer.from_pretrained(\"/opt/tiger/neuronpedia/jacobian-lens/hf_ckpt\")\n# model = jlens.from_hf(hf, tok)\n\n# lens_logits, model_logits, _ = lens.apply(\n#     model, \"Fact: The currency used in the country shaped like a boot is\",\n#     positions=[-2])\n# for layer, logits in sorted(lens_logits.items()):\n#     print(layer, [tok.decode([t]) for t in logits[0].topk(5).indices])\n"}}}}