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1. Identify the given information:
- Initial velocity (v0): 30 ft/sec
- Initial height (y0): 3 feet off the ground
2. Determine the acceleration due to gravity (g) at sea level, which is
approximately -32.2 ft/s^2. Since we're dealing with a short distance and
not very high altitudes, we can consider this value of g as constant.
3. Use Newton's second law of motion to find the differential equation
that describes the motion:
- The net force acting on the ball is the sum of gravity (mg) and air resistance (which we will neglect for simplicity). So, Fnet = mg.
- Since acceleration equals force divided by mass, a = Fnet/m. In this case, m cancels out, so a = g.
- The motion equation becomes: dv/dt = g = -32.2 ft/s^2 (since we're moving upwards). This is the first differential equation.
4. Since we want to describe the ball's position (y) as well, we need
another differential equation. We can use kinematics equations:
- The velocity equation is v = v0 + at. Substitute the value of a from step 3:
dv/dt = -32.2 ft/s^2. Rewriting it as an integral:
∫dv = -32.2 ft/s^2 * dt
- Integrating with respect to time, we get v(t) = -16.1 t + C (constant of integration).
5. Now we have two equations, one for velocity and another for position:
- dv/dt = -32.2 ft/s^2 (equation 1)
- v(t) = -16.1 t + C (equation 2)
6. We need to find the constant C using the initial conditions given in
the problem. We know that initially, at time t=0, the ball's velocity is
v0:
- Equation 2 becomes v0 = -16.1 * 0 + C, which simplifies to C = v0 = 30 ft/sec (initial velocity).
7. Now we have both equations with the constants filled in:
- dv/dt = -32.2 ft/s^2 (equation 1)
- v(t) = -16.1 t + 30 ft/sec (equation 2)
8. Finally, we need to find the ball's position (y) as a function of time:
- Use the second kinematics equation y = y0 + v0*t + ½*a*t^2 (since acceleration is constant, it can be moved outside the integral).
- Substitute the values for v0 and a:
y(t) = 3 ft + (30 ft/sec) * t + (-½ * 32.2 ft/s^2) * t^2
That's it! The differential equation describing the motion of the ball is:
- dv/dt = -32.2 ft/s^2 (equation 1)
- v(t) = -16.1 t + 30 ft/sec (equation 2)
- y(t) = 3 ft + (30 ft/sec) * t - (16.1 t^2) ft (equation 3)Locutusque/Hyperion-3.0-Mistral-7B-DPO is an advanced language model fine-tuned with a dataset of 20,000 meticulously curated high-quality preference pairs using Direct Preference Optimization (DPO). The examples were generated by GPT-4 to ensure exceptional quality and relevance. This model is designed to provide superior performance across a wide range of complex tasks, including question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.Locutusque/Hyperion-3.0-Mistral-7B-DPO model was fine-tuned on a carefully curated dataset of 20,000 preference pairs, where 4,000 examples were used to fine-tune. These examples were generated by GPT-4 to ensure the highest quality and relevance across various domains, including programming, medical texts, mathematical problems, and reasoning tasks. The training data was further optimized using Direct Preference Optimization (DPO) to align the model's outputs with human preferences and improve overall performance.| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu_flan_cot_fewshot | N/A | get-answer | 0 | exact_match | 0.5833 | ± | 0.0118 |
| - mmlu_flan_cot_fewshot_humanities | N/A | get-answer | 0 | exact_match | 0.5039 | ± | 0.0205 |
| - mmlu_flan_cot_fewshot_formal_logic | 0 | get-answer | 0 | exact_match | 0.2143 | ± | 0.1138 |
| - mmlu_flan_cot_fewshot_high_school_european_history | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.1143 |
| - mmlu_flan_cot_fewshot_high_school_us_history | 0 | get-answer | 0 | exact_match | 0.7727 | ± | 0.0914 |
| - mmlu_flan_cot_fewshot_high_school_world_history | 0 | get-answer | 0 | exact_match | 0.5385 | ± | 0.0997 |
| - mmlu_flan_cot_fewshot_international_law | 0 | get-answer | 0 | exact_match | 0.9231 | ± | 0.0769 |
| - mmlu_flan_cot_fewshot_jurisprudence | 0 | get-answer | 0 | exact_match | 0.5455 | ± | 0.1575 |
| - mmlu_flan_cot_fewshot_logical_fallacies | 0 | get-answer | 0 | exact_match | 0.7778 | ± | 0.1008 |
| - mmlu_flan_cot_fewshot_moral_disputes | 0 | get-answer | 0 | exact_match | 0.5526 | ± | 0.0817 |
| - mmlu_flan_cot_fewshot_moral_scenarios | 0 | get-answer | 0 | exact_match | 0.4000 | ± | 0.0492 |
| - mmlu_flan_cot_fewshot_philosophy | 0 | get-answer | 0 | exact_match | 0.7647 | ± | 0.0738 |
| - mmlu_flan_cot_fewshot_prehistory | 0 | get-answer | 0 | exact_match | 0.6571 | ± | 0.0814 |
| - mmlu_flan_cot_fewshot_professional_law | 0 | get-answer | 0 | exact_match | 0.3294 | ± | 0.0362 |
| - mmlu_flan_cot_fewshot_world_religions | 0 | get-answer | 0 | exact_match | 0.8947 | ± | 0.0723 |
| - mmlu_flan_cot_fewshot_other | N/A | get-answer | 0 | exact_match | 0.6833 | ± | 0.0244 |
| - mmlu_flan_cot_fewshot_business_ethics | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_clinical_knowledge | 0 | get-answer | 0 | exact_match | 0.5862 | ± | 0.0931 |
| - mmlu_flan_cot_fewshot_college_medicine | 0 | get-answer | 0 | exact_match | 0.6364 | ± | 0.1050 |
| - mmlu_flan_cot_fewshot_global_facts | 0 | get-answer | 0 | exact_match | 0.6000 | ± | 0.1633 |
| - mmlu_flan_cot_fewshot_human_aging | 0 | get-answer | 0 | exact_match | 0.6087 | ± | 0.1041 |
| - mmlu_flan_cot_fewshot_management | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_marketing | 0 | get-answer | 0 | exact_match | 0.8000 | ± | 0.0816 |
| - mmlu_flan_cot_fewshot_medical_genetics | 0 | get-answer | 0 | exact_match | 1.0000 | ± | 0.0000 |
| - mmlu_flan_cot_fewshot_miscellaneous | 0 | get-answer | 0 | exact_match | 0.8023 | ± | 0.0432 |
| - mmlu_flan_cot_fewshot_nutrition | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.0833 |
| - mmlu_flan_cot_fewshot_professional_accounting | 0 | get-answer | 0 | exact_match | 0.4839 | ± | 0.0912 |
| - mmlu_flan_cot_fewshot_professional_medicine | 0 | get-answer | 0 | exact_match | 0.5806 | ± | 0.0901 |
| - mmlu_flan_cot_fewshot_virology | 0 | get-answer | 0 | exact_match | 0.3889 | ± | 0.1182 |
| - mmlu_flan_cot_fewshot_social_sciences | N/A | get-answer | 0 | exact_match | 0.7003 | ± | 0.0239 |
| - mmlu_flan_cot_fewshot_econometrics | 0 | get-answer | 0 | exact_match | 0.4167 | ± | 0.1486 |
| - mmlu_flan_cot_fewshot_high_school_geography | 0 | get-answer | 0 | exact_match | 0.9091 | ± | 0.0627 |
| - mmlu_flan_cot_fewshot_high_school_government_and_politics | 0 | get-answer | 0 | exact_match | 0.8095 | ± | 0.0878 |
| - mmlu_flan_cot_fewshot_high_school_macroeconomics | 0 | get-answer | 0 | exact_match | 0.6512 | ± | 0.0735 |
| - mmlu_flan_cot_fewshot_high_school_microeconomics | 0 | get-answer | 0 | exact_match | 0.5769 | ± | 0.0988 |
| - mmlu_flan_cot_fewshot_high_school_psychology | 0 | get-answer | 0 | exact_match | 0.9000 | ± | 0.0391 |
| - mmlu_flan_cot_fewshot_human_sexuality | 0 | get-answer | 0 | exact_match | 0.6667 | ± | 0.1421 |
| - mmlu_flan_cot_fewshot_professional_psychology | 0 | get-answer | 0 | exact_match | 0.6522 | ± | 0.0578 |
| - mmlu_flan_cot_fewshot_public_relations | 0 | get-answer | 0 | exact_match | 0.5833 | ± | 0.1486 |
| - mmlu_flan_cot_fewshot_security_studies | 0 | get-answer | 0 | exact_match | 0.4074 | ± | 0.0964 |
| - mmlu_flan_cot_fewshot_sociology | 0 | get-answer | 0 | exact_match | 0.8182 | ± | 0.0842 |
| - mmlu_flan_cot_fewshot_us_foreign_policy | 0 | get-answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_stem | N/A | get-answer | 0 | exact_match | 0.4866 | ± | 0.0262 |
| - mmlu_flan_cot_fewshot_abstract_algebra | 0 | get-answer | 0 | exact_match | 0.0909 | ± | 0.0909 |
| - mmlu_flan_cot_fewshot_anatomy | 0 | get-answer | 0 | exact_match | 0.4286 | ± | 0.1373 |
| - mmlu_flan_cot_fewshot_astronomy | 0 | get-answer | 0 | exact_match | 0.5625 | ± | 0.1281 |
| - mmlu_flan_cot_fewshot_college_biology | 0 | get-answer | 0 | exact_match | 0.5000 | ± | 0.1291 |
| - mmlu_flan_cot_fewshot_college_chemistry | 0 | get-answer | 0 | exact_match | 0.5000 | ± | 0.1890 |
| - mmlu_flan_cot_fewshot_college_computer_science | 0 | get-answer | 0 | exact_match | 0.2727 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_college_mathematics | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| - mmlu_flan_cot_fewshot_college_physics | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1521 |
| - mmlu_flan_cot_fewshot_computer_security | 0 | get-answer | 0 | exact_match | 0.7273 | ± | 0.1408 |
| - mmlu_flan_cot_fewshot_conceptual_physics | 0 | get-answer | 0 | exact_match | 0.6538 | ± | 0.0951 |
| - mmlu_flan_cot_fewshot_electrical_engineering | 0 | get-answer | 0 | exact_match | 0.7500 | ± | 0.1118 |
| - mmlu_flan_cot_fewshot_elementary_mathematics | 0 | get-answer | 0 | exact_match | 0.7317 | ± | 0.0701 |
| - mmlu_flan_cot_fewshot_high_school_biology | 0 | get-answer | 0 | exact_match | 0.5938 | ± | 0.0882 |
| - mmlu_flan_cot_fewshot_high_school_chemistry | 0 | get-answer | 0 | exact_match | 0.3636 | ± | 0.1050 |
| - mmlu_flan_cot_fewshot_high_school_computer_science | 0 | get-answer | 0 | exact_match | 0.5556 | ± | 0.1757 |
| - mmlu_flan_cot_fewshot_high_school_mathematics | 0 | get-answer | 0 | exact_match | 0.3103 | ± | 0.0874 |
| - mmlu_flan_cot_fewshot_high_school_physics | 0 | get-answer | 0 | exact_match | 0.2353 | ± | 0.1060 |
| - mmlu_flan_cot_fewshot_high_school_statistics | 0 | get-answer | 0 | exact_match | 0.3043 | ± | 0.0981 |
| - mmlu_flan_cot_fewshot_machine_learning | 0 | get-answer | 0 | exact_match | 0.4545 | ± | 0.1575 |
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mmlu_flan_cot_fewshot | N/A | get-answer | 0 | exact_match | 0.5833 | ± | 0.0118 |
| - mmlu_flan_cot_fewshot_humanities | N/A | get-answer | 0 | exact_match | 0.5039 | ± | 0.0205 |
| - mmlu_flan_cot_fewshot_other | N/A | get-answer | 0 | exact_match | 0.6833 | ± | 0.0244 |
| - mmlu_flan_cot_fewshot_social_sciences | N/A | get-answer | 0 | exact_match | 0.7003 | ± | 0.0239 |
| - mmlu_flan_cot_fewshot_stem | N/A | get-answer | 0 | exact_match | 0.4866 | ± | 0.0262 |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "Locutusque/Hyperion-3.0-Mistral-7B-DPO"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name)
6
7# For a text generation task
8input_text = "<|im_start|>user\nExplain the implications of quantum entanglement in layman's terms.<|im_end|>\n<|im_start|>assistant\n"
9input_ids = tokenizer.encode(input_text, return_tensors="pt")
10
11# Generate a response
12outputs = model.generate(input_ids, max_length=200, do_sample=True, top_p=0.7, top_k=6) # These are the recommended sample settings.
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))