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What parameters/prompts did you use, I was able to get ok results but not comparable to chat gpt or even bard

by gmm1990 3y ago

rushingcreek 3y ago
We used no prompt (only autocomplete) for the HumanEval reproduction: https://huggingface.co/Phind/Phind-CodeLlama-34B-v1 https://huggingface.co/Phind/Phind-CodeLlama-34B-v1. But you should be able to get it to do stuff just by telling it what you want. Note that it's completion tuned (not chat-tuned), so it should perform better on single-turn conversations.
syntaxing 3y ago
I used textgen, instruct mode with LLaMa 2 template, “simple” parameters. What quantization method are you using?
- gmm1990 3y ago
  I used the llama.cpp quantize q_0 option. Seemed to vary widely just adjusting the temperature parameters. Was giving it an example db query saving to file in json and asking create a similar function did multiple queries.
  - syntaxing 3y ago
    I’m assuming typo and you mean 4? What model size? As mentioned above, I had serious issues making GGUF work well. I’m gonna post an article talking about a better way to set it up. Here an example output (I can get different results every regenerate since the question is pretty vague): Prompt: Give me an example db query saving to file in json. Result: Sure! Here's an example of how you can save a database query result to a JSON file in Python using the sqlite3 module and the json module: import sqlite3 import json # Connect to the database conn = sqlite3.connect("example.db") cursor = conn.cursor() # Execute a query that returns a single row cursor.execute("SELECT \* FROM mytable WHERE id=123") result = cursor.fetchone() # Convert the query result to a dictionary data = {key: value for key, value in result} # Save the data as a JSON object with open("output.json", "w") as f: json.dump(data, f) This code connects to a SQLite database named example.db, executes a query that retrieves a single row from a table called mytable, converts the resulting tuple into a dictionary, and then saves the dictionary as a JSON object to a file named output.json.

What parameters/prompts did you use, I was able to get ok results but not comparable to chat gpt or even bard

We used no prompt (only autocomplete) for the HumanEval reproduction: https://huggingface.co/Phind/Phind-CodeLlama-34B-v1 https://huggingface.co/Phind/Phind-CodeLlama-34B-v1. But you should be able to get it to do stuff just by telling it what you want. Note that it's completion tuned (not chat-tuned), so it should perform better on single-turn conversations.

syntaxing 3y ago

I used textgen, instruct mode with LLaMa 2 template, “simple” parameters. What quantization method are you using?

gmm1990 3y ago
I used the llama.cpp quantize q_0 option. Seemed to vary widely just adjusting the temperature parameters. Was giving it an example db query saving to file in json and asking create a similar function did multiple queries.
- syntaxing 3y ago
  I’m assuming typo and you mean 4? What model size? As mentioned above, I had serious issues making GGUF work well. I’m gonna post an article talking about a better way to set it up. Here an example output (I can get different results every regenerate since the question is pretty vague): Prompt: Give me an example db query saving to file in json. Result: Sure! Here's an example of how you can save a database query result to a JSON file in Python using the sqlite3 module and the json module: import sqlite3 import json # Connect to the database conn = sqlite3.connect("example.db") cursor = conn.cursor() # Execute a query that returns a single row cursor.execute("SELECT \* FROM mytable WHERE id=123") result = cursor.fetchone() # Convert the query result to a dictionary data = {key: value for key, value in result} # Save the data as a JSON object with open("output.json", "w") as f: json.dump(data, f) This code connects to a SQLite database named example.db, executes a query that retrieves a single row from a table called mytable, converts the resulting tuple into a dictionary, and then saves the dictionary as a JSON object to a file named output.json.