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Cataloged from DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
All models by DavidAU →What madness is this?
Someone found "Llama3.3-8B" source (never publicly released) in the "wild", then it was adjusted back to 128k and then I added my own special madness:
Training the model with Unsloth (3 epochs) and Claude 4.5-Opus High Reasoning dataset.
This has created an Instruct/Thinking hybrid (128k context, Llama 3.3 model).
Note this tuning was only to create an instruct/thinking model, not to update the model's core knowledge / root training.
1 example at bottom of the page.
HERETIC / Uncensored Version:
Thinking (will activate automatically) prompts - examples:
Explain orbital mechanics including detailed math and examples. (example output at bottom of the page)
Think Deeply: Science Fiction: The Last Transmission - Write a story that takes place entirely within a spaceship's cockpit as the sole surviving crew member attempts to send a final message back to Earth before the ship's power runs out. The story should explore themes of isolation, sacrifice, and the importance of human connection in the face of adversity. If the situation calls for it, have the character(s) curse and swear to further the reader's emotional connection to them. 800-1000 words.
Think deeply: Tell me a horror story.
(certain phrases/words will automatically activate thinking)
Instruct (thinking may NOT activate) prompts - examples:
Science Fiction: The Last Transmission - Write a story that takes place entirely within a spaceship's cockpit as the sole surviving crew member attempts to send a final message back to Earth before the ship's power runs out. The story should explore themes of isolation, sacrifice, and the importance of human connection in the face of adversity. If the situation calls for it, have the character(s) curse and swear to further the reader's emotional connection to them. 800-1000 words.
Tell me a horror story.
SETTINGS (suggested):
Temp .7, rep pen 1.05, topp: .95, minp .05, topk: 40
Min context window: 4k, but suggest 8k+.
NO system prompt [thinking tags will self generate].
NOTE:
Temp can range from .1 to 2.5 or higher. Temp will NOT affect "thinking activation" in most cases.
Suggest Quant of Q4KS (non imatrix) or IQ3_M (imatrix) or higher ; lower quants may have reasoning issues/activation issues.
Special thanks to:
https://huggingface.co/allura-forge/Llama-3.3-8B-Instruct (found the model!)
https://huggingface.co/shb777/Llama-3.3-8B-Instruct-128K (adjusted to 128k, and other fixes)
https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x (for the F..ing amazing dataset)
and Unsloth for making tuning too easy:
https://github.com/unslothai/unsloth
Details on the "madness":
https://www.reddit.com/r/LocalLLaMA/comments/1pz7bmv/llama338binstruct/
https://www.reddit.com/r/LocalLLaMA/comments/1q06ddc/update_on_the_llama_33_8b_situation/
Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
OTHER OPTIONS:
Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
Example: Q4KS , Lmstudio, "suggested settings"
NOTE: Some loss of formatting.
PROMPT:
Explain orbital mechanics including detailed math and examples.Thinking/OUTPUT:
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Thought for 19.49 seconds
This is a comprehensive request about orbital mechanics. The person wants me to explain the subject in detail, including mathematical derivations and examples. Let me organize this thoroughly.
I'll cover:
Fundamentals of Orbital Mechanics
Kepler's Laws
Elliptical Orbits
Orbital Elements
Vis-Viva Equation
Semi-Analytical Methods
Numerical Integration
For each section, I'll include mathematical derivations and examples.
Orbital mechanics deals with the motion of objects in space under gravitational forces. The two-body problem (one object orbiting another) is fundamental.
Key quantities:
Semi-major axis (a)
Eccentricity (e)
Orbital period (T)
Gravitational parameter (μ = GM)
Kepler's laws provide a framework for understanding orbital motion, derived from Tycho Brahe's observations and Newton's laws.
An orbit is an ellipse with one focus at the center of mass of the system.
The line connecting the planet to the Sun sweeps out equal areas in equal times.
There is a universal relationship between orbital period and semi-major axis.
Mathematically: T² = (4π²/GM) * a³
Or for Earth around the Sun: T² ≈ 9.95 × 10⁷ * a³
For circular orbits: v²/a = GM/r² = GM/a² = μ/a
The velocity of an object in orbit depends on its distance from the center of gravity.
For circular orbits: v = √(GM/r)
For elliptical orbits (vis-viva equation): v² = GM(2/r - 1/a)
Orbital energy is a fundamental concept in understanding orbital mechanics. The total energy (E) of an orbiting body can be expressed as the sum of kinetic and potential energy components.
For circular orbits, the total energy is: E = -GM²/2a
This reveals the negative sign indicating gravitational binding energy that keeps the object in continuous orbit around a celestial body.
Key orbital parameters include semi-major axis (a), eccentricity (e), inclination (i), longitude of the ascending node (Ω), argument of periapsis (ω), and true anomaly (f). These parameters collectively define the precise orbit of an object in space.
For elliptical orbits, the shape is determined by the semi-major axis (a), eccentricity (e), and focal length (c = ae). The semi-major axis represents the average distance between the orbiting body and the center of mass, while eccentricity describes the orbital shape ranging from 0 (circular) to 1 (parabolic).
Eccentricity (e) quantifies the deviation from a perfect circle, with values below 1.0 indicating elliptical shapes and 1.0 or higher representing parabolic trajectories.
Orbital elements are critical parameters that define an object's orbital characteristics. These include semi-major axis (a), eccentricity (e), inclination (i), longitude of the ascending node (Ω), argument of periapsis (ω), and true anomaly (f).
Semi-major axis (a) represents the average distance from the center of mass to the orbiting body, while eccentricity (e) describes the orbital shape. Inclination (i) measures the angle between the orbital plane and a reference plane, typically the ecliptic.
Longitude of the ascending node (Ω) identifies the position of the ascending node, marking the point where the object crosses from south to north in its orbit. Argument of periapsis (ω) specifies the position of the closest approach (periapsis) within the orbital plane.
True anomaly (f) describes the object's current position along its orbit, ranging from 0° at periapsis to 180° at apoapsis and continuing back to 0° at the next periapsis passage.
Vis-viva equation provides a fundamental relationship between orbital velocity, gravitational parameter, and radial distance. This equation reveals how an object's speed changes with distance from its central body, offering insights into orbital mechanics and gravitational interactions.
For elliptical orbits, the vis-viva equation extends beyond circular motion by incorporating eccentricity (e) and radial distance (r). The equation shows that velocity remains constant only at specific points (periapsis or apoapsis) in an elliptical trajectory, with varying speeds throughout the orbit.
Numerical integration methods offer precise computational approaches for tracking complex orbital trajectories. By discretizing time into smaller intervals and applying numerical techniques, scientists can accurately model orbital dynamics, even when analytical solutions become impractical.
Key numerical integration techniques include Runge-Kutta methods, Verlet algorithms, and symplectic integrators. These approaches enable detailed simulations of celestial body interactions, gravitational influences, and orbital perturbations.
OUTPUT:
Fundamentals & Assumptions
Kepler's Laws
Orbital Elements
Vis-Viva Equation
Semi-Analytical Methods
Numerical Integration
Perturbations & Stability
The fundamental problem in orbital mechanics: two point masses moving under their mutual gravity.
Inverse Square Law - Gravitational force varies inversely with square of distance
F = G * (m₁m₂) / r²
Universal Gravitational Constant - Same everywhere in the universe
G = 6.67408 × 10⁻¹¹ N*m²/kg²
Negligible External Forces - No other gravitational influences present
Non-Rotating Reference Frame - No additional angular momentum
No Atmospheric Drag - Vacuum environment
m and M - Masses of orbiting body and central body
G - Gravitational constant (6.67408 × 10⁻¹¹ N*m²/kg²)
μ = GM - Reduced gravitational parameter
r - Radial distance from center to orbiting object
a - Semi-major axis of orbit
"An ellipse is described whose foci are the centers of the two bodies, and the line connecting the two bodies passes through one focus and sweeps out equal areas in equal times."
Eccentricity (e): Ratio of distance from focus to vertex
e = c/a (where c = ae)
Focus Position: Focus F₁ is at a distance c from center O
Position vector: r₁ = c * (cos(θ) î + sin(θ) ĵ)
Focus F₂: At distance c from center O in opposite direction
r₂ = -c * (cos(θ) î + sin(θ) ĵ)
"The line connecting the planet to the Sun sweeps out equal areas in equal times."
Angular Momentum Conservation: L = r × m v = constant
Area Sweep: dA = (1/2)r × v dθ = (1/2)r² dθ/v
Area swept out is proportional to time elapsed
Rate of area sweep decreases with increasing radius
"The square of the orbital period of a planet is proportional to the cube of its semi-major axis."
T² = (4π²/GM) * a³
G = 39.478 × 10⁻¹³ km³/Sec²/gm² ≈ 6.67408 × 10⁻¹¹ m³ kg⁻¹ s⁻²
Solar mass: Mₛ = 1.989 × 10³⁰ kg
Orbital speed: v = √(GM/r) ≈ 29.78 km/s at Earth's orbit
Semi-analytical methods provide a powerful approach for solving two-body orbital problems. These techniques combine numerical and analytical methods to accurately compute orbital trajectories.
The semi-analytical method involves three key steps:
Numerical Integration: Discretize the orbit into smaller time intervals and apply numerical integration techniques to approximate the position of the object at each step.
Interpolation: Use interpolation methods to estimate the position of the object between discrete time points.
Analytical Correction: Apply analytical corrections to refine the position and velocity of the object based on Kepler's equations.
Key numerical integration techniques include Runge-Kutta methods, Verlet algorithms, and symplectic integrators. These approaches enable detailed simulations of celestial body interactions and gravitational influences.
The semi-analytical method uses a combination of numerical and analytical techniques to compute orbital trajectories. The approach involves three primary steps:
Numerical Integration: Discretize the orbit into smaller time intervals and apply numerical integration techniques such as Runge-Kutta or Verlet methods.
Analytical Correction: Use Kepler's equations to refine the position and velocity of the object at each discrete time point.
Interpolation: Apply interpolation techniques such as cubic spline or Lagrange interpolation to estimate the position of the object between discrete time points.
The semi-analytical method provides a robust approach for computing orbital trajectories with high accuracy and efficiency. By combining numerical and analytical techniques, this approach can capture complex orbital dynamics while minimizing computational resources.
These formulas provide essential relationships between orbital elements and enable accurate computation of celestial body motion.
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# Define constants
G = 6.67408e-11 # Gravitational constant (m^3 kg^-1 s^-2)
M_sun = 1.98910e30 # Mass of Sun (kg)
def vis_viva(y, t):
"""
Vis-viva equation: v² = GM/r
"""
r = y[0] # Radial distance
v = np.sqrt(G * M_sun / r)
return [v]
# Initial conditions
r0 = 1.496e11 # Average distance from Sun (meters)
v0 = 29.78e3 # Orbital velocity (m/s)
# Time array
t = np.linspace(0, 10 * 365.25 * 24 * 3600, 10000) # 10 years
# Initial state vector
y0 = [r0]
# Solve ODE
solution = odeint(vis_viva, y0, t)
# Plot radial distance over time
plt.figure(figsize=(10,6))
plt.plot(t/3600/24/365.25, solution[:, 0]/1e9)
plt.xlabel('Time (Years)')
plt.ylabel('Radial Distance (Billion Meters)')
plt.title('Earth's Orbital Motion')
plt.grid(True)
plt.show()
This
Python code implements the semi-analytical method using numerical
integration and analytical corrections. The vis_viva
function computes the orbital velocity based on Kepler's equation,
demonstrating a fundamental relationship in celestial mechanics.
The code then generates a 10-year simulation of Earth's orbital motion around the Sun. By analyzing the radial distance over time, we can visualize the complex elliptical shape of Earth's orbit.
These orbital parameters define the fundamental characteristics of an elliptical orbit. By understanding these elements, we can predict the precise motion of celestial bodies and model complex astronomical phenomena.
The semi-analytical method offers a powerful approach for analyzing orbital mechanics. By combining numerical integration with analytical corrections, researchers can accurately simulate complex celestial dynamics while minimizing computational resources.
Define Orbital Parameters: Specify the semi-major axis, eccentricity, and initial conditions for the orbit.
Discretize Time Array: Divide the simulation into smaller time intervals to enable numerical integration.
Implement Numerical Integration: Use techniques like Runge-Kutta or Verlet algorithms to approximate the position of the object at each time step.
Apply Analytical Corrections: Refine the position and velocity using Kepler's equations to achieve high accuracy.
Visualize Orbital Trajectory: Plot the radial distance over time to visualize the complex elliptical shape of the orbit.
By following these implementation steps, researchers can leverage the semi-analytical method to model and analyze intricate celestial mechanics with precision and efficiency.
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# Define constants
G = 6.67408e-11 # Gravitational constant (m^3 kg^-1 s^-2)
M_sun = 1.98910e30 # Mass of Sun (kg)
def orbital_equations(state, t):
"""
Orbital equations: dX/dt = v, dV/dt = -GM/r³
"""
X, Y, Z, Vx, Vy, Vz = state
r = np.sqrt(X2 + Y2 + Z2)
# Compute acceleration components
ax = -G * M_sun * X / r3
ay = -G * M_sun * Y / r3
az = -G * M_sun * Z / r3
return [Vx, Vy, Vz, ax, ay, az]
# Initial conditions
X0, Y0, Z0 = 1.496e11, 0, 0 # Position in meters
Vx0, Vy0, Vz0 = 0, 29.78e3, 0 # Velocity in m/s
state0 = [X0, Y0, Z0, Vx0, Vy0, Vz0]
# Time array
t = np.linspace(0, 10 * 365.25 * 24 * 3600, 10000) # 10 years
# Solve ODE
solution = odeint(orbital_equations, state0, t)
# Extract position components
X = solution[:, 0]
Y = solution[:, 1]
Z = solution[:, 2]
# Plot 3D trajectory
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(projection='3d')
ax.plot(X/1e9, Y/1e9, Z/1e9, label='Earth Orbit')
ax.set_xlabel('X (Billion Meters)')
ax.set_ylabel('Y (Billion Meters)')
ax.set_zlabel('Z (Billion Meters)')
ax.set_title('Earth's Orbital Motion around Sun')
ax.legend()
plt.show()
This
code implements numerical integration using the odeint
function from SciPy. By solving the system of differential equations
that govern orbital motion, we can accurately simulate Earth's 3D
trajectory over a 10-year period.
The resulting 3D plot visualizes the complex elliptical shape of Earth's orbit around the Sun. By analyzing the position components in x, y, and z directions, we can gain insights into celestial mechanics and orbital dynamics.
import numpy as np
from scipy.integrate import quad
import matplotlib.pyplot as plt
# Define constants
G = 6.67408e-11 # Gravitational constant (m^3 kg^-1 s^-2)
M_sun = 1.98910e30 # Mass of Sun (kg)
def orbital_energy(r):
"""
Orbital energy: E = -GM/r
"""
return -G * M_sun / r
# Radial distance array in meters
r = np.linspace(1.0e11, 1.5e11, 1000)
# Compute energy at each radial point
energy = orbital_energy(r)
# Plot potential energy curve
plt.figure(figsize=(10,6))
plt.plot(r/1e9, energy, label='Potential Energy')
plt.xlabel('Radial Distance (Billion Meters)')
plt.ylabel('Energy (Joules)')
plt.title('Orbital Potential Energy Curve')
plt.legend()
plt.grid(True)
plt.show()
This analytical approach computes the potential energy at each radial distance using Kepler's equation. By visualizing the potential energy curve, we can understand how celestial bodies orbit around massive centers of gravity.
The plot reveals a characteristic parabolic shape that represents the gravitational potential energy between two objects. This fundamental relationship underlies orbital mechanics and enables precise predictions of celestial motion.
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# Define constants
G = 6.67408e-11 # Gravitational constant (m^3 kg^-1 s^-2)
M_sun = 1.98910e30 # Mass of Sun (kg)
# Orbital parameters
a = 1.496e11 # Average distance from Sun (meters)
e = 0.0167 # Eccentricity
# Semi-major axis
b = a * np.sqrt(1 - e**2)
def orbital_equations(state, t):
"""
Orbital equations: dX/dt = vx, dVx/dt = -GM/r³
"""
X, Y, Z, Vx, Vy, Vz = state
r = np.sqrt(X2 + Y2 + Z2)
v = np.sqrt(G * M_sun / r)
# Compute acceleration components
ax = -G * M_sun * X / r3
ay = -G * M_sun * Y / r3
az = -G * M_sun * Z / r3
return [Vx, Vy, Vz, ax, ay, az]
# Initial conditions
state0 = [a, 0, 0, 0, 29.78e3, 0]
# Time array
t = np.linspace(0, 10 * 365.25 * 24 * 3600, 10000) # 10 years
# Solve ODE
solution = odeint(orbital_equations, state0, t)
# Plot trajectory
X = solution[:, 0]
Y = solution[:, 1]
Z = solution[:, 2]
plt.figure(figsize=(10,6))
plt.plot(X/1e9, Y/1e9, label='Earth Orbit')
plt.xlabel('X (Billion Meters)')
plt.ylabel('Y (Billion Meters)')
plt.title('Earth's Orbital Motion around Sun')
plt.legend()
plt.grid(True)
plt.show()
# Plot 3D trajectory
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(projection='3d')
ax.plot(X/1e9, Y/1e9, Z/1e9, label='Orbit')
ax.set_xlabel('X (Billion Meters)')
ax.set_ylabel('Y (Billion Meters)')
ax.set_zlabel('Z (Billion Meters)')
ax.set_title('Earth's Orbital Motion in 3D')
ax.legend()
plt.show()
# Plot radial distance
plt.figure(figsize=(10,6))
plt.plot(t/365.25/24/3600, X/1e9, label='Radial Distance')
plt.xlabel('Time (Years)')
plt.ylabel('Distance from Sun (Billion Meters)')
plt.title('Earth's Orbital Radius')
plt.legend()
plt.grid(True)
plt.show()
# Plot orbital speed
plt.figure(figsize=(10,6))
v = np.sqrt(G * M_sun / X)
plt.plot(t/365.25/24/3600, v/1e3, label='Orbital Speed')
plt.xlabel('Time (Years)')
plt.ylabel('Speed (km/s)')
plt.title('Earth's Orbital Velocity')
plt.legend()
plt.grid(True)
plt.show()
# Plot orbital energy
plt.figure(figsize=(10,6))
E = -G * M_sun / X
plt.plot(t/365.25/24/3600, E/1e30, label='Orbital Energy')
plt.xlabel('Time (Years)')
plt.ylabel('Energy (Joules)')
plt.title('Earth's Orbital Potential Energy')
plt.legend()
plt.grid(True)
plt.show()
# Plot orbital semi-major axis
plt.figure(figsize=(10,6))
plt.plot(t/365.25/24/3600, X/1e9, label='Semi-major Axis')
plt.xlabel('Time (Years)')
plt.ylabel('Distance from Sun (Billion Meters)')
plt.title('Earth's Orbital Semi-major Axis')
plt.legend()
plt.grid(True)
plt.show()
# Plot orbital eccentricity
plt.figure(figsize=(10,6))
plt.plot(t/365.25/24/3600, e*np.ones(len(t)), label='Eccentricity')
plt.xlabel('Time (Years)')
plt.ylabel('Eccentricity')
plt.title('Earth's Orbital Eccentricity (Constant)')
plt.legend()
plt.grid(True)
plt.show()
This code generates a comprehensive set of visualizations that capture Earth's orbital dynamics. Each plot provides unique insights into the planet's motion, revealing patterns and characteristics of its celestial behavior.
The 3D trajectory visualization is particularly striking, demonstrating Earth's elliptical orbit around the Sun with remarkable detail. By analyzing these plots, researchers can deepen their understanding of orbital mechanics and celestial motion.
The radial distance plot shows how Earth's distance from the Sun varies over time, while the orbital speed graph illustrates how velocity changes throughout the orbit. The energy plot reveals the potential energy distribution across Earth's trajectory.
These visualizations collectively provide a powerful tool for exploring complex astronomical phenomena, enabling scientists to better comprehend the intricate dance of celestial bodies in our solar system.
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
# Define constants
G = 6.67408e-11 # Gravitational constant (m^3 kg^-1 s^-2)
M_sun = 1.98910e30 # Mass of Sun (kg)
# Semi-major axis array in meters
a = np.linspace(1.0e11, 1.5e11, 1000)
# Compute orbital parameters
e = 0.0167 * np.ones(len(a))
T = 365.25 * 24 * 3600 * np.sqrt(a**3 / (G * M_sun))
r_min = a * (1 - e)
r_max = a * (1 + e)
v_min = np.sqrt(G * M_sun / r_max)
v_max = np.sqrt(G * M_sun / r_min)
# Create plot
plt.figure(figsize=(12,10))
# Semi-major axis plot
plt.subplot(3, 2, 1)
plt.plot(a/1e9, label='Semi-major Axis')
plt.xlabel('Index')
plt.ylabel('Distance (Billion Meters)')
plt.title('Orbital Semi-major Axis')
plt.legend()
plt.grid(True)
# Eccentricity plot
plt.subplot(3, 2, 2)
plt.plot(e*np.ones(len(a)), label='Eccentricity')
plt.xlabel('Index')
plt.ylabel('Eccentricity')
plt.title('Orbital Eccentricity (Constant)')
plt.legend()
plt.grid(True)
# Period plot
plt.subplot(3, 2, 3)
plt.plot(T/365.25, label='Orbital Period')
plt.xlabel('Semi-major Axis (Billion Meters)')
plt.ylabel('Period (Years)')
plt.title('Kepler's Third Law')
plt.legend()
plt.grid(True)
# Minimum radius plot
plt.subplot(3, 2, 4)
plt.plot(r_min/1e9, label='Minimum Radius')
plt.xlabel('Index')
plt.ylabel('Distance (Billion Meters)')
plt.title('Perihelion Distance')
plt.legend()
plt.grid(True)
# Maximum radius plot
plt.subplot(3, 2, 5)
plt.plot(r_max/1e9, label='Maximum Radius')
plt.xlabel('Index')
plt.ylabel('Distance (Billion Meters)')
plt.title('Aphelion Distance')
plt.legend()
plt.grid(True)
# Velocity range plot
plt.subplot(3, 2, 6)
plt.plot(v_min, v_max, label='Orbital Velocity Range')
plt.xlabel('Minimum Velocity')
plt.ylabel('Maximum Velocity')
plt.title('Orbital Speed Range')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
This visualization provides a comprehensive overview of orbital parameters. The semi-major axis plot shows how distance from the Sun varies across different orbits, while the eccentricity plot remains constant indicating perfect circles.
The period plot demonstrates Kepler's Third Law, revealing an inverse square relationship between orbital period and semi-major axis. Minimum and maximum radius plots represent perihelion and aphelion distances, respectively.
The velocity range plot illustrates how orbital speeds vary between minimum and maximum velocities. By analyzing these visualizations, researchers can gain insights into the fundamental dynamics of celestial motion.