Transformers
GGUF
44 datasets
6 languages
mistral
text-generation-inference
unsloth
trl
chemistry
biology
legal
art
music
finance
code
medical
Merge
climate
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
metadata
language:
- en
- sw
- ig
- so
- es
- ca
license: apache-2.0
metrics:
- accuracy
- bertscore
- bleu
- brier_score
- cer
- character
- charcut_mt
- chrf
- code_eval
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- code
- farmer
- doctor
- Mega-Series
- Cyber-Series
- Role-Play
- Self-Rag
- ThinkingBot
- milestone
- mega-series
- SpydazWebAI
- llama-cpp
- gguf-my-repo
base_model: LeroyDyer/_Spydaz_Web_AI_
Uploaded model
- Developed by: Leroy "Spydaz" Dyer
- License: apache-2.0
- Finetuned from model : LeroyDyer/LCARS_AI_010
[
https://github.com/spydaz
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.
Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1
- 32k context window (vs 8k context in v0.1)
- Rope-theta = 1e6
- No Sliding-Window Attention
Introduction :
SpydazWeb AI model :
Methods:
Trained for multi-task operations as well as rag and function calling :
This model is a fully functioning model and is fully uncensored:
the model has been trained on multiple datasets on the huggingface hub and kaggle :
the focus has been mainly on methodology :
- Chain of thoughts
- steo by step
- tree of thoughts
- forest of thoughts
- graph of thoughts
- agent generation : Voting, ranking, ...
with these methods the model has gained insights into tasks, enabling for knowldge transfer between tasks :
the model has been intensivly trained in recalling data previously entered into the matrix:
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.