Feature Overview
We need to introduce additional testing upstream to validate that upgrading PyTorch from 2.5.z to 2.6.z in the core InstructLab repo (instructlab/instructlab) does not negatively impact the existing logic. This is because our upstream E2E tests currently only test when "use_dolomite=False", but the downstream product (RHEL AI) now runs with "use_dolomite=True".

With the recent changes published in SDG v0.8.0, we will also need a new config which uses this Llama 70B model. 

Goals 

• Create a new E2E test that runs with "use_dolomite=True"
• Create a new config which uses Llama 70B (we can use an FP8 quantized version that runs on Hopper hardware) and add a pass for SDG which uses this model instead of the default Mixtral

Requirements (mandatory -_ Complete while in Refinement status):
A list of specific needs, capabilities, or objectives that a Feature must deliver to satisfy the Feature. Some requirements will be flagged as MVP. If an MVP gets shifted, the Feature shifts. If a non MVP requirement slips, it does not shift the feature.

Done - Acceptance Criteria (mandatory - Complete while in Refinement status):
Acceptance Criteria articulates and defines the value proposition - what is required to meet the goal and intent of this Feature. The Acceptance Criteria provides a detailed definition of scope and the expected outcomes - from a users point of view
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Use Cases - i.e. User Experience & Workflow: (Initial completion while in Refinement status):
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Background and Strategic Fit (Initial completion while in Refinement status):
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Customer Considerations {}{}(Initial completion while in Refinement status):
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