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License: MIT License
CompanionLLM - A framework to finetune LLMs to be your own sentient conversational companion
License: MIT License
Description: Work on improving the model's responses by making it more context-aware. Enhance the response generation to consider the previous parts of the conversation for more natural and coherent replies.
Skills Needed: Natural language processing, Python, Git
Create a Jupyter notebook for generating the dataset required for fine-tuning the CompanionLLama model. The dataset should be carefully curated, incorporating elements from the original Samantha dataset and additional contextual data to enhance the model's ability to emulate sentience.
Conduct a thorough comparison between the Avalon-Llama-7b model and the Avalo-Mitsral-7b model. Evaluate their performance, capabilities, and responses to assess the strengths and weaknesses of each model.
Description: Review and update the project's documentation. Ensure that it accurately reflects the current state of the project, including setup instructions, contribution guidelines, and code documentation.
Skills Needed: Technical writing, Markdown, Git
Implement a Gradio interface for the CompanionLLama model to allow users to interact with the model through a web-based interface. This can make it more accessible and user-friendly.
Skills Needed: Python, Gradio, Git
Create Jupyter notebooks for fine-tuning the Mitsral 7b LLM using the CompanionLLama dataset and for performing inference with the fine-tuned model. These notebooks will be crucial for refining the model's responses and evaluating its performance.
Thanks for sharing this ๐ https://github.com/adithya-s-k/CompanionLLM/blob/main/Mistral_7B_qLora_Finetuning.ipynb
I learn a lot from that, btw I'm not quite understand what I miss there
Generated instruction
, the 1-3 characters get cut off. e.g . CRE
get cut off and appear ATE
instead of CREATE
Generated instruction
didn't match Ground truth
.Here's what I got.
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Prompt:
<s>
Generate a SQL query to create a table containing Movie information (title, release year, genre).
Not applicable
[INST]
Generated instruction:
ATE TABLE Movies (
id INT PRIMARY KEY,
title VARCHAR(255),
release_year INT,
genre VARCHAR(255)
); [/INST] \n CREATE TABLE Movies (
id INT PRIMARY KEY,
title VARCHAR(255),
release_year INT,
genre VARCHAR(255)
); [/INST]
Ground truth:
CREATE TABLE Movies (
title VARCHAR(50) NOT NULL,
release_year INT NOT NULL,
genre VARCHAR(20)
);
Is this normal? Any hint to improve this?
Thanks
Implement full weights fine-tuning of the CompanionLLama model using the Axalotal framework. This will help enhance the model's performance and adapt it to specific tasks or domains.
Description: Experiment with different hyperparameters and training strategies to optimize the fine-tuning process. Document your findings and suggest the best configuration for fine-tuning the model.
Skills Needed: Machine learning, Python, Git
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