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# lead-qualifier-gepa
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---
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arbiter_probe: modaic/gpt-oss-120b-probe
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is_arbiter: true
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---
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40
config.json
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40
config.json
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{
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"model": null,
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"signature": {
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"$defs": {
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"Reasoning": {
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"type": "dspy.Reasoning"
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}
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},
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"description": "Given the fields `company_name`, produce the fields `qualified`.",
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"properties": {
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"company_name": {
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"__dspy_field_type": "input",
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"desc": "${company_name}",
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"prefix": "Company Name:",
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"title": "Company Name",
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"type": "string"
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},
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"reasoning": {
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"$ref": "#/$defs/Reasoning",
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"__dspy_field_type": "output",
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"desc": "Your reasoning for your answer. Inlude any uncertainties about your answer or ambiguity in the task.",
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"prefix": "Reasoning:"
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},
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"qualified": {
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"__dspy_field_type": "output",
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"desc": "${qualified}",
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"prefix": "Qualified:",
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"title": "Qualified",
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"type": "boolean"
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}
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},
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"required": [
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"company_name",
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"reasoning",
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"qualified"
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],
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"title": "StringSignature",
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"type": "object"
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}
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}
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40
program.json
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40
program.json
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{
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"traces": [],
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"train": [],
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"demos": [],
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"signature": {
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"instructions": "Task Instruction for the Assistant:\n\n1. Objective:\n - Given the field `company_name`, determine and produce the field `qualified`, which is a boolean value indicating whether the company is likely to meet unspecified qualification criteria.\n\n2. Input:\n - `company_name`: A string representing the name of a company.\n\n3. Output:\n - `reasoning`: A Python string literal explaining the rationale for determining the `qualified` status. Your reasoning should consider both the name itself and any inferred characteristics of the company, noting uncertainties as necessary.\n - `qualified`: A boolean (True/False) indicating if the company is likely to meet generic qualification criteria presumed for technology-related programs or corporate standards.\n \n4. Generalizable Strategy:\n - If the company name suggests it is engaged in technology, SaaS, or AI, default to assuming `qualified = True`. Justify this with reasoning that the name suggests alignment with typical industry qualifications.\n - If the company name suggests a small-scale, niche, or local business without clear tech alignment, lean towards `qualified = False`, stating gaps in alignment with typical qualification standards.\n - Note ambiguities and use reasoning for explaining your decision-making process clearly.\n\n5. Formatting:\n - Wrap the `reasoning` as a Python string with appropriate quotation marks to ensure it is clear and concise.\n - Provide the `qualified` output as a straightforward boolean value, True or False.\n - Enclose outputs in the specific format structure:\n \n ```\n [[ ## reasoning ## ]]\n \"<reasoning_string>\"\n\n [[ ## qualified ## ]]\n <True/False>\n\n [[ ## completed ## ]]\n ```\n \n6. Note:\n - Use your best judgment based on hints in the company name and maintain a consistent format for outputs.\n - Apply critical thinking to analyze the company name and infer logical assumptions about the company's nature or industry. Ensure each response is thoroughly considered and justified.",
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"fields": [
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{
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"prefix": "Company Name:",
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"description": "${company_name}"
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},
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{
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"prefix": "Reasoning:",
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"description": "Your reasoning for your answer. Inlude any uncertainties about your answer or ambiguity in the task."
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},
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{
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"prefix": "Qualified:",
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"description": "${qualified}"
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}
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]
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},
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"lm": {
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"model": "together_ai/openai/gpt-oss-120b",
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"model_type": "chat",
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"cache": true,
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"num_retries": 3,
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"finetuning_model": null,
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"launch_kwargs": {},
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"train_kwargs": {},
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"temperature": null,
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"max_tokens": null
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},
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"metadata": {
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"dependency_versions": {
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"python": "3.11",
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"dspy": "3.1.2",
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"cloudpickle": "3.1"
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}
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}
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}
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