From 5928052d6ae4ed128aa7b021616f02ea03eb08e3 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 17 Aug 2026 15:53:13 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: Mungert/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning-GGUF Source: Original Platform --- .gitattributes | 85 + ...g-Claude-4.5-Opus-High-Reasoning-bf16.gguf | 3 + ...ude-4.5-Opus-High-Reasoning-bf16_q8_0.gguf | 3 + ...ng-Claude-4.5-Opus-High-Reasoning-f16.gguf | 3 + ...aude-4.5-Opus-High-Reasoning-f16_q8_0.gguf | 3 + ...laude-4.5-Opus-High-Reasoning-imatrix.gguf | 3 + ...-Claude-4.5-Opus-High-Reasoning-iq2_m.gguf | 3 + ...-Claude-4.5-Opus-High-Reasoning-iq2_s.gguf | 3 + ...Claude-4.5-Opus-High-Reasoning-iq2_xs.gguf | 3 + ...-Claude-4.5-Opus-High-Reasoning-iq3_m.gguf | 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+- sub-plot generation +- fiction writing +- story generation +- scene continue +- storytelling +- fiction story +- science fiction +- romance +- all genres +- story +- writing +- vivid prosing +- vivid writing +- fiction +- roleplaying +- bfloat16 +- role play +- 128k context +- llama3.3 +- llama-3 +- llama-3.3 +- unsloth +- finetune +pipeline_tag: text-generation +library_name: transformers +--- + +# Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning GGUF Models + + +## Model Generation Details + +This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`05fa625ea`](https://github.com/ggerganov/llama.cpp/commit/05fa625eac5bbdbe88b43f857156c35501421d6e). + + + + + +--- + +## Quantization Beyond the IMatrix + +I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides. + +In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here: +👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py) + +While this does increase model file size, it significantly improves precision for a given quantization level. + +### **I'd love your feedback—have you tried this? How does it perform for you?** + + + + +--- + + + Click here to get info on choosing the right GGUF model format + + +--- + + + + + + +

Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning

+ + + +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: + +https://huggingface.co/DavidAU/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning + +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: + +https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be + +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: + +[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] + +You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here: + +[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] + +--- + +Example: Q4KS , Lmstudio, "suggested settings" + +NOTE: Some loss of formatting. + +--- + +PROMPT: + + +Explain orbital mechanics including detailed math and examples. + + +Thinking/OUTPUT: + + + +llama-3.3-8b-instruct-claude-4.5-opus-high-reasoning-checkpoint-90-q4_k_s.gguf

+


+
+ +

+

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:

+
    +

  1. Fundamentals + of Orbital Mechanics

    2. +

  2. Kepler's + Laws

    3. +

  3. Elliptical + Orbits

    4. +

  4. Orbital + Elements

    5. +

  5. Vis-Viva + Equation

    6. +

  6. Semi-Analytical + Methods

    7. +

  7. Numerical + Integration

    8. +
+

For +each section, I'll include mathematical derivations and examples.

+

+1. +Fundamentals of Orbital Mechanics

+

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:

+ +

+2. +Kepler's Laws

+

Kepler's +laws provide a framework for understanding orbital motion, derived +from Tycho Brahe's observations and Newton's laws.

+

First +Law: Law of Ellipses

+

An +orbit is an ellipse with one focus at the center of mass of the +system.

+

Second +Law: Equal Area Rule

+

The +line connecting the planet to the Sun sweeps out equal areas in equal +times.

+

Third +Law: Harmonies of the Spheres

+

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

+

Orbital +Velocity

+

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:

+


+
+ +

+

+Orbital +Mechanics: A Comprehensive Guide

+

+Table +of Contents

+
    +

  1. Fundamentals + & Assumptions

    2. +

  2. Kepler's + Laws

    3. +

  3. Orbital + Elements

    4. +

  4. Vis-Viva + Equation

    5. +

  5. Semi-Analytical + Methods

    6. +

  6. Numerical + Integration

    7. +

  7. Perturbations + & Stability

    8. +
+
+ +

+1. +Fundamentals and Assumptions

+

1.1 +Two-Body Problem

+

The +fundamental problem in orbital mechanics: two point masses moving +under their mutual gravity.

+

Assumptions:

+
    +

  1. Inverse + Square Law - Gravitational force varies inversely with + square of distance

    +
      +

    • F + = G * (m₁m₂) / r²

      • +
    +

  2. Universal + Gravitational Constant - Same everywhere in the + universe

    +
      +

    • G + = 6.67408 × 10⁻¹¹ N*m²/kg²

      • +
    +

  3. Negligible + External Forces - No other gravitational influences + present

    4. +

  4. Non-Rotating + Reference Frame - No additional angular momentum

    5. +

  5. No + Atmospheric Drag - Vacuum environment

    6. +
+

1.2 +Orbital Parameters

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ Parameter

+

+ Description

+

+ Range

+

a

+

Semi-major axis

+

[0, ∞)

+

e

+

Eccentricity

+

[0, 1]

+

T

+

Orbital period

+

[0, ∞)

+

r

+

Radial distance

+

[0, a]

+

v

+

Orbital velocity

+

[0, v₀ = √(GM/a)]

+
+

Notation:

+ +
+ +

+2. +Kepler's Laws of Planetary Motion

+

2.1 +First Law: Law of Ellipses

+

+"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."

+

Mathematical +Formulation:

+ +

Proof:

+ + + + + + + + + + + + + + + +

+ Step

+

+ Description

+

+ Formula

+

1

+

Define ellipse with foci + at O and O'

+

x²/a² + y²/b² = 1

+
+ + + + + + + + + + + + + + + + + + + + +

2

+

Parameterize with angle + θ

+

x = a * cos(θ), y = b * + sin(θ)

+

3

+

Apply trigonometric + identity

+

cos²(θ) + sin²(θ) = + 1

+
+ + + + + + + + + + + + + + + +

4

+

Express semi-major axis + (a) and eccentricity (e)

+

a² = b²/a² → b² = + a²(1-e²)

+
+ + + + + + + + + + + + + + + +

5

+

Derive equation of + ellipse in standard form

+

x²/a² + y²/b² = 1

+
+

Key +Equations:

+ + + + + + + + + + + + + + + + + +

+ Equation

+

+ Description

+

r = a * (1 - e²)/[1 + e + * cos(θ)]

+

Radial distance from + focus

+

T = 2π√(a³/μ)

+

Orbital period for + elliptical orbit

+
+

2.2 +Second Law: Equal Area Rule

+

+"The +line connecting the planet to the Sun sweeps out equal areas in equal +times."

+

Mathematical +Proof:

+
    +

  1. Angular + Momentum Conservation: L = r × m v = constant

    2. +

  2. Area + Sweep: dA = (1/2)r × v dθ = (1/2)r² dθ/v

    3. +
+ + + + + + + + + + + + + + + +

+ Step

+

+ Description

+

+ Formula

+

3

+

Relate angular momentum + to area sweep

+

dA/dt = r²/(2v) dθ/dt

+
+ + + + + + + + + + + + + + + +

4

+

Substitute v from energy + equation

+

dA/dt = r²/(2v) * + (GM/r²)/r

+
+ + + + + + + + + + + + + + + +

5

+

Simplify to show + proportional relationship

+

dA/dt ∝ r/v ∝ 1/r

+
+

Geometric +Interpretation:

+ +

2.3 +Third Law: Harmonies of the Spheres

+

+"The +square of the orbital period of a planet is proportional to the cube +of its semi-major axis."

+

Mathematical +Formulation:

+

T² += (4π²/GM) * a³

+ + + + + + + + + + + + + + + + + + + + + + + + + +

+ Planet

+

+ Semi-major Axis (AU)

+

+ Orbital Period (Years)

+

Mercury

+

0.387

+

0.241

+

Venus

+

0.723

+

0.615

+

Earth

+

1.000

+

1.000

+
+

Universal +Gravitational Constant (G):

+ +

Derived +Values:

+ +
+ +

+3. +Orbital Elements

+

3.1 +Semi-Analytical Method Overview

+

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:

+
    +

  1. Numerical + Integration: Discretize the orbit into smaller time + intervals and apply numerical integration techniques to approximate + the position of the object at each step.

    2. +

  2. Interpolation: + Use interpolation methods to estimate the position of the object + between discrete time points.

    3. +

  3. Analytical + Correction: Apply analytical corrections to refine the + position and velocity of the object based on Kepler's equations.

    4. +
+

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.

+

3.2 +Semi-Analytical Formulation

+

The +semi-analytical method uses a combination of numerical and analytical +techniques to compute orbital trajectories. The approach involves +three primary steps:

+
    +

  1. Numerical + Integration: Discretize the orbit into smaller time + intervals and apply numerical integration techniques such as + Runge-Kutta or Verlet methods.

    2. +

  2. Analytical + Correction: Use Kepler's equations to refine the position + and velocity of the object at each discrete time point.

    3. +

  3. Interpolation: + Apply interpolation techniques such as cubic spline or Lagrange + interpolation to estimate the position of the object between + discrete time points.

    4. +
+

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.

+

3.3 +Key Semi-Analytical Formulas

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ Formula

+

+ Description

+

r = a * (1 - e²)/(1 + e + * cos(θ))

+

Radial distance from + focus

+

v = √(GM/a) * (1 - + e²)/(1 + e * cos(θ))

+

Orbital velocity

+

T = 2π√(a³/μ)

+

Orbital period

+

f = arctan((r² + b² - + a²)/2ab)

+

True anomaly

+

E = √(GM/a) * (1 - + e²)/(1 + e * cos(θ))

+

Eccentric anomaly

+
+

These +formulas provide essential relationships between orbital elements and +enable accurate computation of celestial body motion.

+

3.4 +Semi-Analytical Implementation

+
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.

+

3.5 +Implementation Details

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ Parameter

+

+ Description

+

+ Range

+

a

+

Semi-major axis

+

[0, ∞)

+

e

+

Eccentricity

+

[0, 1]

+

T

+

Orbital period

+

[0, ∞)

+

r

+

Radial distance

+

[0, a]

+

v

+

Orbital velocity

+

[0, v₀ = √(GM/a)]

+
+

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.

+

3.6 +Key Implementation Steps

+
    +

  1. Define + Orbital Parameters: Specify the semi-major axis, + eccentricity, and initial conditions for the orbit.

    2. +

  2. Discretize + Time Array: Divide the simulation into smaller time + intervals to enable numerical integration.

    3. +

  3. Implement + Numerical Integration: Use techniques like Runge-Kutta or + Verlet algorithms to approximate the position of the object at each + time step.

    4. +

  4. Apply + Analytical Corrections: Refine the position and velocity + using Kepler's equations to achieve high accuracy.

    5. +

  5. Visualize + Orbital Trajectory: Plot the radial distance over time to + visualize the complex elliptical shape of the orbit.

    6. +
+

By +following these implementation steps, researchers can leverage the +semi-analytical method to model and analyze intricate celestial +mechanics with precision and efficiency.

+

3.7 +Numerical Implementation

+
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(X**2 + Y**2 + Z**2)
+    
+    # Compute acceleration components
+    ax = -G * M_sun * X / r**3
+    ay = -G * M_sun * Y / r**3
+    az = -G * M_sun * Z / r**3
+    
+    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.

+

3.8 +Analytical Approach

+
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.

+

3.9 +Visualization Techniques

+
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(X**2 + Y**2 + Z**2)
+    v = np.sqrt(G * M_sun / r)
+    
+    # Compute acceleration components
+    ax = -G * M_sun * X / r**3
+    ay = -G * M_sun * Y / r**3
+    az = -G * M_sun * Z / r**3
+    
+    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.

+

3.10 +Orbital Parameter Visualization

+
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.

+


+ + + +--- + +# 🚀 If you find these models useful + +Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: + +👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) + + +The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) + +💬 **How to test**: + Choose an **AI assistant type**: + - `TurboLLM` (GPT-4.1-mini) + - `HugLLM` (Hugginface Open-source models) + - `TestLLM` (Experimental CPU-only) + +### **What I’m Testing** +I’m pushing the limits of **small open-source models for AI network monitoring**, specifically: +- **Function calling** against live network services +- **How small can a model go** while still handling: + - Automated **Nmap security scans** + - **Quantum-readiness checks** + - **Network Monitoring tasks** + +🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): +- ✅ **Zero-configuration setup** +- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. +- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate! + +### **Other Assistants** +🟢 **TurboLLM** – Uses **gpt-4.1-mini** : +- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. +- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents** +- **Real-time network diagnostics and monitoring** +- **Security Audits** +- **Penetration testing** (Nmap/Metasploit) + +🔵 **HugLLM** – Latest Open-source models: +- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. + +### 💡 **Example commands you could test**: +1. `"Give me info on my websites SSL certificate"` +2. `"Check if my server is using quantum safe encyption for communication"` +3. `"Run a comprehensive security audit on my server"` +4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution! + +### Final Word + +I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. + +If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone. + +I'm also open to job opportunities or sponsorship. + +Thank you! 😊 diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file