Not selecting the tokens with the highest probabilities with temperature 0

# Prerequisites

Please answer the following questions for yourself before submitting an issue.

- [x] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [x] I carefully followed the [README.md](https://github.com/abetlen/llama-cpp-python/blob/main/README.md).
- [x] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [x] I reviewed the [Discussions](https://github.com/abetlen/llama-cpp-python/discussions), and have a new bug or useful enhancement to share.

# Expected Behavior

When running Mistral LLM with a temperature of 0, I expected the model to choose tokens with the highest probabilities exclusively.

# Current Behavior

I observed that for some tokens (apparently randomly), it selected the ones with the second-highest probabilities, leading to different outputs.

# Environment and Context

I am using Google Colab, T4 GPU with 15 GB VRAM.

```
$ python3 --version
Python 3.10.12
$ make --version
GNU Make 4.3
$ g++ --version
g++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
$ pip list | egrep "pandas|numpy"
geopandas 0.13.2
numpy 1.25.2
pandas 1.5.3
pandas-datareader 0.10.0
pandas-gbq 0.19.2
pandas-stubs 1.5.3.230304
sklearn-pandas 2.2.0
```

# Steps to Reproduce

1. Installing llama-cpp-python
```
!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python==0.2.57
```

2. Initializing the model
```
from llama_cpp import Llama
llm = Llama(
model_path="mistral-7b-instruct-v0.2.Q8_0.gguf",
n_ctx= 10000,
n_gpu_layers = -1,
verbose= True,
logits_all = True # true to return logprobs
)
```
3. Running the model and getting the outputs
```
prompt = """[INST] (((5 + 4)*2)/9)+2? [/INST] """

llm.reset() # to clear the cache
output = llm.create_completion(prompt,
max_tokens = 200,
echo = False,
temperature = 0,
logprobs = 5 ) # to return top 5 tokens

results = output['choices'][0]['text']
print('Results from the model:\n')
print(results)

log_probs = output['choices'][0]['logprobs']['top_logprobs']
print('Results by selecting tokens with the highest probabilities:\n')
for el in log_probs:
chosen = max(el, key=lambda k: el[k])
print(chosen, end = '')

```

The results of the above two prints are the following which are not exactly the same because of the selected tokens:
```
Results from the model:

To solve the expression step by step, follow these instructions:

1. First, perform the multiplication inside the parentheses: (5 + 4) * 2 = 9 * 2 = 18.
2. Next, do the division: 18 / 9 = 2.
3. Add 2 to the result: 2 + 2 = 4.

So, the solution to ((5 + 4)*2)/9+2 is 4.

Results by selecting tokens with the highest probabilities:

To solve the expression ((( by step, follow the instructions:

1. First, perform the multiplication inside the parentheses: (5 + 4) * 2 = 9 * 2 = 18.
2. Next, do the division: 18 / 9 = 2.
3. Add 2 to the result: 2 + 2 = 4.

So, the solution to the5 + 4)*2)/9 +2 is 4.
```

4. Coverting log_probs to probabilities

```
import numpy as np
# convert logits to probs
for dict in log_probs:
for key, value in dict.items():
dict[key] = np.exp(value)
```

The probabilities show that for example, the model selects the token `step` with probability 0.22 instead of the token `(((` with probability 0.54.
```
{' (((': 0.5391272,
' step': 0.22150521,
' ((': 0.15229335,
',': 0.06753055,
'(((': 0.010963313}
```
As another example, it selects `+` with probability 0.0002 instead of `""+` (with leading whitespace) with probability 0.99 in this expression : `((5 + 4)*2)/9+2 is 4`
```
{' +': 0.9997909,
'+': 0.00020875783,
' plus': 2.6573096e-07,
' ±': 2.159923e-08,
' +=': 1.6546656e-08},
```