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| Name | Quant method | Size |
|---|---|---|
| Hyperion-3.0-Yi-34B.Q2_K.gguf | Q2_K | 11.94GB |
| Hyperion-3.0-Yi-34B.IQ3_XS.gguf | IQ3_XS | 13.26GB |
| Hyperion-3.0-Yi-34B.IQ3_S.gguf | IQ3_S | 13.99GB |
| Hyperion-3.0-Yi-34B.Q3_K_S.gguf | Q3_K_S | 13.93GB |
| Hyperion-3.0-Yi-34B.IQ3_M.gguf | IQ3_M | 14.5GB |
| Hyperion-3.0-Yi-34B.Q3_K.gguf | Q3_K | 15.51GB |
| Hyperion-3.0-Yi-34B.Q3_K_M.gguf | Q3_K_M | 15.51GB |
| Hyperion-3.0-Yi-34B.Q3_K_L.gguf | Q3_K_L | 16.89GB |
| Hyperion-3.0-Yi-34B.IQ4_XS.gguf | IQ4_XS | 17.36GB |
| Hyperion-3.0-Yi-34B.Q4_0.gguf | Q4_0 | 18.13GB |
| Hyperion-3.0-Yi-34B.IQ4_NL.gguf | IQ4_NL | 18.3GB |
| Hyperion-3.0-Yi-34B.Q4_K_S.gguf | Q4_K_S | 18.25GB |
| Hyperion-3.0-Yi-34B.Q4_K.gguf | Q4_K | 19.24GB |
| Hyperion-3.0-Yi-34B.Q4_K_M.gguf | Q4_K_M | 19.24GB |
| Hyperion-3.0-Yi-34B.Q4_1.gguf | Q4_1 | 20.1GB |
| Hyperion-3.0-Yi-34B.Q5_0.gguf | Q5_0 | 22.08GB |
| Hyperion-3.0-Yi-34B.Q5_K_S.gguf | Q5_K_S | 22.08GB |
| Hyperion-3.0-Yi-34B.Q5_K.gguf | Q5_K | 22.65GB |
| Hyperion-3.0-Yi-34B.Q5_K_M.gguf | Q5_K_M | 22.65GB |
| Hyperion-3.0-Yi-34B.Q5_1.gguf | Q5_1 | 24.05GB |
| Hyperion-3.0-Yi-34B.Q6_K.gguf | Q6_K | 26.28GB |
| Hyperion-3.0-Yi-34B.Q8_0.gguf | Q8_0 | 34.03GB |
v(t) = -32t + 30
Here, v(t) represents the velocity of the ball at time t (in feet per second), and the negative sign indicates that the velocity decreases due to gravity. Now, we can integrate the velocity function to find the position function:
h(t) = ∫v(t) dt = ∫(-32t + 30) dt
Integrating each term separately, we get:
h(t) = (-16t^2 + 30t + C)
To determine the constant C, we use the initial condition that the ball is thrown from 3 feet above the ground. At t=0, the height is 3 feet:
h(0) = 3
Substituting t=0 into the equation for h(t):
3 = (-16(0)^2 + 30(0) + C)
Solving for C, we get:
C = 3
So the final equation for the height of the ball as a function of time is:
h(t) = -16t^2 + 30t + 31from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "Locutusque/Hyperion-3.0-Yi-34B"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(model_name)
6
7# For a text generation task
8input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|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, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))