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Model: charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: teknium/OpenHermes-2.5-Mistral-7B
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datasets:
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- bunkalab/topic_based_chatml_dpo_pairs
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library_name: Bunkatopics
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widget:
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- text: Tell a danish joke in french
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pipeline_tag: text-generation
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---
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## Model description
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TopicNeuralHermes 2.5 Mistral 7B is a refined model developed through fine-tuning with a specific subset of data, selected via Topic Modeling Techniques using [Bunkatopics](https://github.com/charlesdedampierre/BunkaTopics), as a continuing from [OpenHermes 2.5](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B).
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The model was trained on a refined DPO dataset. The objective was to train the model on a small portion of the DPO data. To achieve this, we compared two datasets used to train the reward model: the rejected Llama answers and the accepted ChatGPT answers from the [DPO dataset](mlabonne/chatml_dpo_pairs).
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We then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one.
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Our hypothesis is that these topics encapsulate the main differences between the two answering styles.
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This method allows for quicker convergence with significantly less data (around 1/6 of the initial dataset). The Dataset can be found at [bunkalab/topic_based_chatml_dpo_pairs](https://huggingface.co/datasets/bunkalab/topic_based_chatml_dpo_pairs)
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Special thanks to [mlabonne](https://huggingface.co/mlabonne) for creating the [colab notebook](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing#scrollTo=YpdkZsMNylvp) that facilitated the DPO Strategy.
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Results of the model can be found here: We do as well as similar models with way less data and computing power :)
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## Topic Analysis
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We applied the topic modeling method to both datasets, extracting 30 topics from each.
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These topics were characterized using the 10 most specific unigrams or bigrams.
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We then compared the two sets of topics (30 from each dataset) and retained those in the accepted dataset that shared fewer than 2 terms with any topic in the rejected dataset
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We found the 13 distinctive following topics described by 10 terms each:
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**Emotional Dynamics**: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.
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**Global Knowledge Queries**: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.
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**Digital Interactions and Queries**: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.
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**Business and Cybersecurity**: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.
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**Lifestyle and Wellness**: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.
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**Wildlife Ecology**: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.
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**Environmental Science and Climate**: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.
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**Maritime and Mechanical Engineering**: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.
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**Cultural and Social Dynamics**: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.
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**Political Media Analysis**: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.
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**International Relations and Policy**: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.
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**Astrophysics and Physical Sciences**: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.
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**Film Critique and Analysis**: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.
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While those topics are not domain-specific, they did not appear right away in the rejected dataset. Further research need to undersand the reason behind the prominence of
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those topics in the accepted dataset.
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## Usage
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You can run this model using LM Studio or any other frontend.
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You can also run this model using the following code:
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```python
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import transformers
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from transformers import AutoTokenizer
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# Format prompt
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message = [
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{"role": "system", "content": "You are a helpful assistant chatbot."},
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{"role": "user", "content": "What is Topic Modeling?"}
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]
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tokenizer = AutoTokenizer.from_pretrained('charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B')
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
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# Create pipeline
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pipeline = transformers.pipeline(
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"text-generation",
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model='charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B',
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tokenizer=tokenizer
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)
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# Generate text
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sequences = pipeline(
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prompt,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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num_return_sequences=1,
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max_length=200,
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)
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print(sequences[0]['generated_text'])
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```
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## Training hyperparameters
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**LoRA**:
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* r=16
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* lora_alpha=16
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* lora_dropout=0.05
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* bias="none"
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* task_type="CAUSAL_LM"
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* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
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**Training arguments**:
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* per_device_train_batch_size=4
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* gradient_accumulation_steps=4
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* gradient_checkpointing=True
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* learning_rate=5e-5
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* lr_scheduler_type="cosine"
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* max_steps=200
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* optim="paged_adamw_32bit"
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* warmup_steps=100
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**DPOTrainer**:
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* beta=0.1
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* max_prompt_length=1024
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* max_length=1536
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You can find the results of the running on Weights & Biases: https://wandb.ai/bunka/huggingface/runs/xq59p47g?workspace=user-charlesdedampierre
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## Model Family Tree
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