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---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
datasets:
- gate369/Alpaca-Star
---
- QSTAR
- I think this model is proof of my theory that you dont need a special architecture to train a llm to reason. The techniques i imployed to make this could
be greatly expanded on and coupled with a agent system to achieve a functioning low cost agi system.
- Steps: -Loss:
- 1 - 1.373000
- 2 - 1.551400
- 3 - 1.083100
- 4 - 1.164900
- 5 - 1.196500
- 6 - 1.015400
- ....
- 363 - 0.019000
- 364 - 0.017600
- 365 - 0.015600
- 366 - 0.016700
- 367 - 0.016400
- 368 - 0.016600
- 369 - 0.017600
- heres a conversation i had with the model:
- user: "hi there. id like to build a time machine. how might i go about achieving this?"
- assistant: "|startthought| To build a time machine, I need to figure out how to move through time just like we move through space. That means finding a way to change my
frame of reference from the stationary Earth to one that's moving forward in time. |endthought| One approach is to use Einstein's theory of relativity and massive
objects with strong gravitational fields, like black holes or neutron stars. By warping spacetime, these objects can slow down time for an observer passing by.
Another idea is to explore quantum mechanics and the notion of entanglement between particles. If I could create a pair of entangled particles and keep one, the
other might appear elsewhere in space-time, effectively teleporting it through time. Yet another approach is to look at cosmic rays and high-energy particle
collisions, which can create short-lived particles that seem to violate the forward-in-time direction of time. Understanding these phenomena might lead to a
way to control time flow. Ultimately, building a functional time machine will require advancing our understanding of these fundamental physical principles and
finding a way to apply them technologically. It's a grand challenge that has captivated scientists and science fiction writers alike for centuries."
- user: "how might we make use of this phenomena to create a functioning time machine?"
- assistant: "|startthought| To make use of these phenomenas to create a funcitoning time machine, we need to find a way to control and harness them technologically.
That could mean developing advanced gravitational lensing techniques to manipulate space-time around massive objects, exploring quantum entanglement
in more complex systems, or finding a way to generate the high-energy particle collision conditions needed to create tachyons. It's likely that we'll need to
understand and combine multiple approaches to make a functional time machine a reality. |endthought| One approach might be to focus on developing advanced
gravitational lensing techniques. This could allow us to control the warping of space-time around massive objects like black holes or neutrino stars, creating
stable time vortexes that we can pass through. Another idea is to explore quantum entanglement in more complex systems. If we could create and maintain a
large-scale entangled state between two particles, we might be able to use one particle as a clock while the other appears elsewhere in space-time, effectively
teleporting it through time. Yet another approach is to look at high-energy particle collision phenomenas like those created in large hadron colliders or
supernova explosions. These events can generate short-lived particles called tachyons, which seem to move faster than the speed of light and violate the
forward-in-time direction of time. If we could understand and control this phenomena, we might be able to create a stable source of tachyons that could be
used to power a time machine. Ultimately, making a functional time machine will require advancing our understanding of these fundamental physical principles
and finding a way to apply them technologically. It's a grand challenge that has captivaed scientists and science fiction writers alike for centuries."
- the data set will need more work but this shows insane potential
# Uploaded model
- **Developed by:** liminerity
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This model was trained on a alpaca based dataset i made using perplexity and claude 3. The dataset is meant to encourage the llm the think before speaking
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |