Row 4855

Row ID: 4855 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 4855 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Heard from two independent sources at MSFT (one close to Sebastien Bubeck) about the upcoming Phi-3 models:

* Three different sized models (up to 14B) * Again, mostly synthetic and LLM-augmented training data * Apparently some upscaling techniques on the training side * No more Apache 2 but more restrictive license (similar to llama3) * Mixtral level performance with much fewer parameters

I wanted to see if anyone has more insider information about the models.

FieldValue
text Heard from two independent sources at MSFT (one close to Sebastien Bubeck) about the upcoming Phi-3 models: * Three different sized models (up to 14B) * Again, mostly synthetic and LLM-augmented training data * Apparently some upscaling techniques on the training side * No more Apache 2 but more restrictive license (similar to llama3) * Mixtral level performance with much fewer parameters I wanted to see if anyone has more insider information about the models.
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-04-23
username_encoded Z0FBQUFBQm5LakwyOFB1WmhtV2JpU3ZCZjBGVXJxNkJJeVFnYVFiWnVrQWFnZ1AwR3hrREUydDdCOHlaS0x1bjZrTDFEcXNVN2JST1pnV0RBbU1meFNiZlV0T1J5UEltLUE9PQ==
url_encoded Z0FBQUFBQm5Lak9GckIzT19teXpPbGsyUFh5eDRUdmhKaUt2V3JPYXdOQkpzTnNQRnppYjN5TWJ5UW9GOEN3UGF4QS16RkNWajl3aEhRdTVVSS1yVEtqTTBZc1EyRy1BWWhEQ2h5dk5xR21NWi1BdGRDMDQ0dW9PaGg0OG1zbmQ0NktIOG1pc2JzLVdnY0drNjdaczJZUkNBalJuWXNsNVJBZ1VaV3N6RGJOb2tJMWlST2dUa2htaVBmUFNSZFNiT1E2UnJjN1poLUFr

Raw Record

{
  "text": "Heard from two independent sources at MSFT (one close to Sebastien Bubeck) about the upcoming Phi-3 models:\n\n* Three different sized models (up to 14B)\n* Again, mostly synthetic and LLM-augmented training data\n* Apparently some upscaling techniques on the training side\n* No more Apache 2 but more restrictive license (similar to llama3)\n* Mixtral level performance with much fewer parameters\n\nI wanted to see if anyone has more insider information about the models.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-04-23",
  "username_encoded": "Z0FBQUFBQm5LakwyOFB1WmhtV2JpU3ZCZjBGVXJxNkJJeVFnYVFiWnVrQWFnZ1AwR3hrREUydDdCOHlaS0x1bjZrTDFEcXNVN2JST1pnV0RBbU1meFNiZlV0T1J5UEltLUE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GckIzT19teXpPbGsyUFh5eDRUdmhKaUt2V3JPYXdOQkpzTnNQRnppYjN5TWJ5UW9GOEN3UGF4QS16RkNWajl3aEhRdTVVSS1yVEtqTTBZc1EyRy1BWWhEQ2h5dk5xR21NWi1BdGRDMDQ0dW9PaGg0OG1zbmQ0NktIOG1pc2JzLVdnY0drNjdaczJZUkNBalJuWXNsNVJBZ1VaV3N6RGJOb2tJMWlST2dUa2htaVBmUFNSZFNiT1E2UnJjN1poLUFr"
}

Entry Information