Fine-tuning continues training a pretrained model on a smaller, domain-specific dataset to teach a skill or behavior—not new facts. It can shape a finance product’s writing style when prompting and retrieval-augmented generation are insufficient. section 3.3.3
A practical adaptation plan:
- Curate a small, high-quality set of paired inputs and desired outputs; the AlphaStream team prepares 200 examples with summaries written by a human analyst. section 3.3.3
- Consider parameter-efficient methods such as LoRA, adapters, or prefix tuning instead of updating every model parameter, which can be expensive and time-consuming. section 3.3.3
AlphaStream summaries
To make community-post digests sound like AlphaStream, the team pairs raw posts with summaries in the platform’s desired tone. section 3.3.3
Table 3.1 Parameter-efficient fine-tuning techniques
|
Method
|
How it works
|
BFSI considerations
|
|---|---|---|
|
LoRA
|
Inserts low-rank matrices into a few key layers, freezing most of the model’s original weights. Only these small added matrices are trained.
|
Lowers GPU memory usage compared to a full fine-tune; feasible if HPC costs are a concern. Potentially simpler to revert or reapply for new base model versions.
|
|
Adapters
|
Adds small, trainable “adapter” modules in each transformer layer; the base model remains frozen.
|
Well-known approach in NLP that often requires minimal HPC resources. BFSI teams must ensure consistent brand voice or disclaimers across the entire pipeline.
|
|
Prefix Tuning (p-Tuning)
|
Learns special “prefix tokens” that guide the model’s hidden states, with no changes to the main model parameters.
|
Suitable for injecting official disclaimers or short policy text. Less ideal for deeply specialized tasks such as complex derivatives. Easy to discard or layer on top of new models.
|
|
QLoRA [4] and Hybrids
|
Combines LoRA with quantization (e.g., 8-bit or 4-bit) to reduce memory usage further; may also use other PEFT combinations.
|
Especially relevant for on-premises HPC constraints: BFSI can handle bigger base models at lower precision, though quantization demands careful validation of numeric stability.
|
Compare PEFT methods by how they adapt a model and the financial-sector considerations attached to each. section 3.3.3
