Arc53/DocsGPT-7B

This model is fine tuned on top of llama-2-7b

DocsGPT is optimized for Documentation: Specifically fine-tuned for providing answers that are based on documentation provided in context, making it particularly useful for developers and technical support teams.

We used 50k high quality examples to finetune it over 1.5 days on A10G GPU. We used lora fine tuning process.

Its an apache-2.0 license so you can use it for commercial purposes too.

How to run it

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "Arc53/docsgpt-7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Benchmarks are still WIP

To prepare your prompts make sure you keep this format:

 ### Instruction
(where the question goes)
### Context
(your document retrieval + system instructions)
### Answer

Here is an example comparing it to meta-llama/Llama-2-7b

Prompt:

### Instruction
Create a mock request to /api/answer in python

### Context
You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
Use the following pieces of context to help answer the users question. If its not relevant to the question, provide friendly responses.
You have access to chat history, and can use it to help answer the question.
When using code examples, use the following format:
`` ` `` (language)
(code)
`` ` ``

----------------


/api/answer
Its a POST request that sends a JSON in body with 4 values. Here is a JavaScript fetch example
It will recieve an answer for a user provided question

`` ` ``
// answer (POST http://127.0.0.1:5000/api/answer)
fetch("http://127.0.0.1:5000/api/answer", {
      "method": "POST",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
      "body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
      "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
})
.then((res) => res.text())
.then(console.log.bind(console))
`` ` ``

In response you will get a json document like this one:

`` ` ``
{
  "answer": " Hi there! How can I help you?\\n",
  "query": "Hi",
  "result": " Hi there! How can I help you?\\nSOURCES:"
}
`` ` ``

 

/api/docs_check
It will make sure documentation is loaded on a server (just run it everytime user is switching between libraries (documentations)
Its a POST request that sends a JSON in body with 1 value. Here is a JavaScript fetch example

`` ` ``
// answer (POST http://127.0.0.1:5000/api/docs_check)
fetch("http://127.0.0.1:5000/api/docs_check", {
      "method": "POST",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
      "body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
})
.then((res) => res.text())
.then(console.log.bind(console))
`` ` ``

In response you will get a json document like this one:
`` ` ``
{
  "status": "exists"
}
`` ` ``




Issues and Pull requests

We value contributions to our issues in form of discussion or suggestion, we recommend that you check out existing issues and our Roadmap

If you want to contribute by writing code there are few things that you should know before doing it:
We have frontend (React, Vite) and Backend (python)


### Answer

docsgpt-7b:

To create a mock request to `/api/answer` in Python, you can use the `requests` library. Here's an example:

`` ` ``python
import requests

# Set up the URL and headers
url = "http://127.0.0.1:5000/api/answer"
headers = {
    "Content-Type": "application/json; charset=utf-8"
}

# Set up the JSON data to send in the request body
data = {
    "question": "Hi",
    "history": null,
    "api_key": "OPENAI_API_KEY",
    "embeddings_key": "OPENAI_API_KEY",
    "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"
}

# Send the request and get the response
response = requests.post(url, headers=headers, json=data)

# Print the response
print(response.text)
`` ` ``

This will send a POST request to `/api/answer` with the specified `question`, `history`, `api_key`, `embeddings_key`, and `active_docs` parameters. The response will be printed to the console.

Note that you will need to replace `127.0.0.1:5000` with the actual URL and port of your server. Also, make sure you have the necessary dependencies installed for `requests`.

The original meta-llama/Llama-2-7b:

### DocsGPT


### DocsGPT


### DocsGPT


### DocsGPT