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Model: charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: teknium/OpenHermes-2.5-Mistral-7B
+datasets:
+- bunkalab/topic_based_chatml_dpo_pairs
+library_name: Bunkatopics
+widget:
+- text: Tell a danish joke in french
+pipeline_tag: text-generation
+---
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c13d74f02ef5b95e0e448e/LCntraaGmEF6W7I9DEA-1.png)
+
+## Model description
+
+
+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).
+
+
+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). 
+We then conducted topic modeling on both datasets, keeping only the topics that existed in the accepted dataset but not in the rejected one. 
+Our hypothesis is that these topics encapsulate the main differences between the two answering styles.
+
+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)
+
+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.
+
+Results of the model can be found here: We do as well as similar models with way less data and computing power :)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c13d74f02ef5b95e0e448e/TOEijYNgtY6B7U9Pn29gL.png)
+
+## Topic Analysis
+
+We applied the topic modeling method to both datasets, extracting 30 topics from each. 
+These topics were characterized using the 10 most specific unigrams or bigrams. 
+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
+
+We found the 13 distinctive following topics described by 10 terms each:
+
+
+**Emotional Dynamics**: feelings, Quinn, Austin, minority women, teaching, schools, individual, personality, backgrounds, triggers.
+
+**Global Knowledge Queries**: question, information, geography, news articles, Step, answer, capital city, pipeline system, country, analogy.
+
+**Digital Interactions and Queries**: questions, question, PersonX, modem, answers, effect relationship, Quora, browser, answer, e-commerce.
+
+**Business and Cybersecurity**: email, businesses, initiatives, innovation, advertising papers, spam, breaches, antivirus, payments, prospects.
+
+**Lifestyle and Wellness**: sleep, exercise, gifts, shopping, Casey, stores, stress, headaches, options, mood.
+
+**Wildlife Ecology**: birds, prey, animals, species, infection, nest, eggs, bacteria, insects, kitty condo.
+
+**Environmental Science and Climate**: temperature, gases, greenhouse, emissions, perturbation, sulfur, dioxide, climate change, water, heat.
+
+**Maritime and Mechanical Engineering**: ship, bowling, propulsion, beam width, Filing cabinet, LED, lane, containment area, lawnmower, rotors.
+
+**Cultural and Social Dynamics**: Lindsey, museum, Kate, Rachel, Jason, Alex, Erin, conversation, Laura, exhibits.
+
+**Political Media Analysis**: media platforms, election, politics, teenagers, elections, White House, Barack Obama, nation, Confederate, depression.
+
+**International Relations and Policy**: cooperation, EU, nations, alliance, NATO, European Union, member states, policy, monarch, Brexit.
+
+**Astrophysics and Physical Sciences**: electrons, km, Moon, acceleration, orbit, friction, current, asteroid, electron, collector emitter.
+
+**Film Critique and Analysis**: movie review, film, reviewer, sentiment, critic, flaws, DVD, plot, opinion, originality.
+
+
+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 
+those topics in the accepted dataset.
+
+
+## Usage
+You can run this model using LM Studio or any other frontend.
+
+You can also run this model using the following code:
+
+```python
+import transformers
+from transformers import AutoTokenizer
+
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful assistant chatbot."},
+    {"role": "user", "content": "What is Topic Modeling?"}
+]
+tokenizer = AutoTokenizer.from_pretrained('charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B')
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+
+# Create pipeline
+pipeline = transformers.pipeline(
+    "text-generation",
+    model='charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B',
+    tokenizer=tokenizer
+)
+
+# Generate text
+sequences = pipeline(
+    prompt,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    num_return_sequences=1,
+    max_length=200,
+)
+print(sequences[0]['generated_text'])
+```
+
+
+## Training hyperparameters
+
+**LoRA**:
+* r=16
+* lora_alpha=16
+* lora_dropout=0.05
+* bias="none"
+* task_type="CAUSAL_LM"
+* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
+
+**Training arguments**:
+* per_device_train_batch_size=4
+* gradient_accumulation_steps=4
+* gradient_checkpointing=True
+* learning_rate=5e-5
+* lr_scheduler_type="cosine"
+* max_steps=200
+* optim="paged_adamw_32bit"
+* warmup_steps=100
+
+**DPOTrainer**:
+* beta=0.1
+* max_prompt_length=1024
+* max_length=1536
+
+
+You can find the results of the running on Weights & Biases: https://wandb.ai/bunka/huggingface/runs/xq59p47g?workspace=user-charlesdedampierre
+
+
+## Model Family Tree
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63c13d74f02ef5b95e0e448e/MDtFeO_SoigL748c6xTmc.png)

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config.json

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+{
+  "_name_or_path": "teknium/OpenHermes-2.5-Mistral-7B",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.35.2",
+  "use_cache": false,
+  "vocab_size": 32002
+}

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+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "transformers_version": "4.35.2"
+}

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special_tokens_map.json

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+{
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+    "rstrip": false,
+    "single_word": false
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+}

tokenizer_config.json

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+  "tokenizer_class": "LlamaTokenizer",
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