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library_name: transformers
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: apache-2.0
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base_model:
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- nbeerbower/Yiet-9B
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datasets:
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- flammenai/FlameMix-DPO-v1
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- flammenai/Grill-preprod-v1_chatML
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- flammenai/Grill-preprod-v2_chatML
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---
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![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png)
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# Mahou-1.2-yi-7B
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Mahou is our attempt to build a production-ready conversational/roleplay LLM.
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Future versions will be released iteratively and finetuned from flammen.ai conversational data.
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### Chat Format
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This model has been trained to use ChatML format.
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```
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<|im_start|>system
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{{system}}<|im_end|>
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<|im_start|>{{char}}
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{{message}}<|im_end|>
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<|im_start|>{{user}}
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{{message}}<|im_end|>
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```
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# Roleplay Format
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- Speech without quotes.
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- Actions in `*asterisks*`
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```
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*leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass.
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```
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### ST Settings
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1. Use ChatML for the Context Template.
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2. Turn on Instruct Mode for ChatML.
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3. Use the following stopping strings: `["<", "|", "<|", "\n"]`
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### Method
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Finetuned using an A100 on Google Colab.
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[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
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### Configuration
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LoRA, model, and training settings:
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```python
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# LoRA configuration
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peft_config = LoraConfig(
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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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)
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# Model to fine-tune
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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load_in_4bit=True
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)
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model.config.use_cache = False
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# Reference model
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ref_model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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load_in_4bit=True
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)
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# Training arguments
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training_args = TrainingArguments(
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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=2000,
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save_strategy="no",
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logging_steps=1,
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output_dir=new_model,
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optim="paged_adamw_32bit",
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warmup_steps=100,
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bf16=True,
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report_to="wandb",
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)
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# Create DPO trainer
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dpo_trainer = DPOTrainer(
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model,
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ref_model,
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args=training_args,
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train_dataset=dataset,
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tokenizer=tokenizer,
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peft_config=peft_config,
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beta=0.1,
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force_use_ref_model=True
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)
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# Fine-tune model with DPO
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dpo_trainer.train()
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```
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