coder model tests

[Ahmed Morsi]

Thanks for releasing the coder model. I have done some testing on it

➜  Downloads llm run xwincoder-13b.Q6_K
>>> """q = lambda l: l if len(l) <= 1 else q([x for x in l[1:] if x < l[0]]) + [l[0]] + q([x for x in l[1:] if x >= l[0]])
... show me step by step how the function execution flow for this given input [10,55,6,5]

 Here's a step-by-step explanation of the function execution for the given input [10, 55, 6, 5]:

1. The function is called with the input list [10, 55, 6, 5].
2. Since len(l) <= 1 (in this case, l = [10, 55, 6, 5]), the base condition returns the original list.
3. The function is called recursively with the first element removed from the list and the remaining elements filtered based on their relation to the first element.
   a. For the input list [10, 55, 6, 5], the first element is 10.
   b. Elements less than 10: None (since there are no such elements)
   c. Elements greater than or equal to 10: 55 and 6
4. The function is called recursively with the filtered list [55, 6] as the new input list.
   a. For the input list [55, 6], the first element is 55.
   b. Elements less than 55: None (since there are no such elements)
   c. Elements greater than or equal to 55: 6
5. The function returns the concatenation of the filtered lists and the original element, i.e., [10] + [55] + [6].
6. The final output is the concatenated list [10, 55, 6].

>>> """Describe what this code is doing and make you answer structured:
... q = lambda l: l if len(l) <= 1 else q([x for x in l[1:] if x < l[0]]) + [l[0]] + q([x for x in l[1:] if x >= l[0]])

 This code defines a lambda function `q` that takes a list `l` as input and performs the following operations:

1. If the length of the list `l` is less than or equal to 1, return^C

>>> Describe what this code is doing and make you answer structured:
... q = lambda l: l if len(l) <= 1 else q([x for x in l[1:] if x < l[0]]) + [l[0]] + q([x for x in l[1:] if x >= l[0]])
Use Ctrl-D or /bye to exit.
...
➜  Downloads llm run xwincoder-13b.Q6_K
>>> """q = lambda l: l if len(l) <= 1 else q([x for x in l[1:] if x < l[0]]) + [l[0]] + q([x for x in l[1:] if x >= l[0]])
... the code is doing a popular algorithm do you recognize it ?

 I recognize this as the "QuickSelect" algorithm, which is an efficient in-place variation of the quicksort algorithm. It is used to find the k-th smallest element in an unordered list. In this case, it
appears to be modified to work with lists instead of numbers. The code calculates the median of a list by recursively partitioning the list into smaller and larger sublists, and then combining the
results.

>>> no it is not the "QuickSelect" algorithm

 I see. It seems there was some confusion in understanding the code. The given code is actually a modified version of the "Selection Sort" algorithm, which sorts an unordered list by repeatedly finding
the minimum element from the unsorted part and putting it at the beginning. In this case, it appears to be modified to work with lists instead of numbers. The code calculates the median of a list by
recursively partitioning the list into smaller and larger sublists, and then combining the results.

➜  Downloads llm run xwincoder-13b.Q6_K
>>> Here is a table:
... | Quote | Price | Increase or decrease | Trading volume | Opening price | Highest price | Lowest price |
... |------------|-------|--------|---------|--------| --------|--------|
... | Stock A | 10.2 | +2.3% | 56,789 | 9.8 | 10.5 | 9.7 |
... | Stock B | 23.5 | -1.5% | 45,678 | 24.0 | 24.2 | 23.3 |
... | Stock C | 5.6 | -0.8% | 34,567 | 5.7 | 5.8 | 5.5 |
... | FuturesX | 330.0 | -0.2% | 12,345 | 331.0 | 332.5 | 328.0 |
... | Futures Y | 520.0 | +1.5% | 23,456 | 510.0 | 525.0 | 508.0 |
... | Futures Z | 890.0 | -0.1% | 7,890 | 891.0 | 891.5 | 884.5 |
... | Exchange rate USD/CNY | 6.45 | -0.3% | - | 6.48 | 6.50 | 6.44 |
... | Exchange rate EUR/CNY | 7.67 | +0.2% | - | 7.66 | 7.69 | 7.65 |
... | Gold | 1340 | +0.5% | 1.234 million | 1335.5 | 1345.5 | 1335.5 |
... | Crude oil | 68.2 | +1.0% | 567,000 | 67.5 | 68.9 | 67.3 |
...
... Answer the following questions based on this table:
... Extract the contents of the "Highest Price" column from the table.
 To extract the contents of the "Highest Price" column from the table, we can see that the table has 5 rows with stock data and 1 row with future data. The highest price for each stock is as follows:

1. Stock A: 10.5
2. Stock B: 24.2
3. Stock C: 5.8
4. FuturesX: 332.5
5. Futures Y: 525.0
6. Futures Z: 891.5
7. Exchange rate USD/CNY: 6.50
8. Exchange rate EUR/CNY: 7.69

>>> write the json for 5th and 7 rows
 To write the JSON for the 5th and 7th rows, we first need to extract the relevant information from the table. Here is the JSON format for the 5th and 7th rows:

```json
[
  {
    "Quote": "FuturesY",
    "Price": 520.0,
    "Increase_or_decrease": "+1.5%",
    "Trading_volume": 23456,
    "Opening_price": 510.0,
    "Highest_price": 525.0,
    "Lowest_price": 508.0
  },
  {
    "Quote": "Exchange rate EUR/CNY",
    "Price": 7.67,
    "Increase_or_decrease": "+0.2%",
    "Trading_volume": null,
    "Opening_price": 7.66,
    "Highest_price": 7.69,
    "Lowest_price": 7.65
  }
]

[Han Hu]

Thank you for sharing your test case! We are continually refining the model. Please stay tuned :)