Text Generation
Transformers
Safetensors
mistral
Mistral_Star
Mistral_Quiet
Mistral
Mixtral
Question-Answer
Token-Classification
Sequence-Classification
SpydazWeb-AI
chemistry
biology
legal
code
climate
medical
text-generation-inference
Not-For-All-Audiences
chain-of-thought
tree-of-knowledge
forest-of-thoughts
visual-spacial-sketchpad
alpha-mind
knowledge-graph
entity-detection
encyclopedia
wikipedia
stack-exchange
Reddit
Cyber-series
MegaMind
Cybertron
SpydazWeb
Spydaz
LCARS
star-trek
mega-transformers
Mulit-Mega-Merge
Multi-Lingual
Afro-Centric
African-Model
Ancient-One
Inference Endpoints
Update README.md
Browse files
README.md
CHANGED
@@ -183,6 +183,7 @@ the model can also generate markdown charts with mermaid.
|
|
183 |
|
184 |
The initial phase involved training the model on binary yes/no questions without any explicit methodology. This was crucial in establishing a baseline for the model’s decision-making capabilities.
|
185 |
The model was first trained using a simple production prompt, known as Prompt A, which provided basic functionality. Although this prompt was imperfect, it fit the dataset and set the stage for further refinement.
|
|
|
186 |
## Methodology Development:
|
187 |
|
188 |
The original prompt was later enhanced with a more flexible approach, combining elements from a handcrafted GPT-4.0 prompt. This adaptation aligned the model with my personal agent system, allowing it to better respond to diverse tasks and methodologies.
|
|
|
183 |
|
184 |
The initial phase involved training the model on binary yes/no questions without any explicit methodology. This was crucial in establishing a baseline for the model’s decision-making capabilities.
|
185 |
The model was first trained using a simple production prompt, known as Prompt A, which provided basic functionality. Although this prompt was imperfect, it fit the dataset and set the stage for further refinement.
|
186 |
+
|
187 |
## Methodology Development:
|
188 |
|
189 |
The original prompt was later enhanced with a more flexible approach, combining elements from a handcrafted GPT-4.0 prompt. This adaptation aligned the model with my personal agent system, allowing it to better respond to diverse tasks and methodologies.
|