Migel Tissera
commited on
Commit
•
4b8e0c0
1
Parent(s):
5f57df3
image added
Browse files
README.md
CHANGED
|
@@ -4,7 +4,7 @@ license: apache-2.0
|
|
| 4 |
|
| 5 |
# HelixNet
|
| 6 |
|
| 7 |
-
[HelixNet](https://huggingface.co/migtissera/HelixNet/resolve/main/HelixNet.png)
|
| 8 |
|
| 9 |
HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
|
| 10 |
|
|
|
|
| 4 |
|
| 5 |
# HelixNet
|
| 6 |
|
| 7 |
+
|
| 8 |
|
| 9 |
HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
|
| 10 |
|