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README.md ADDED
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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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+ - microsoft/Phi-3-medium-128k-instruct
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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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+
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+ # Mahou-1.2-phi-14B
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+
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+ Please note: this is an untested, experimental release.
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+
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+ Mahou is our attempt to build a production-ready conversational/roleplay LLM.
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+
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+ Future versions will be released iteratively and finetuned from flammen.ai conversational data.
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+
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+ ### Chat Format
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+
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+ This model has been trained to use ChatML format.
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+
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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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+
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+ # Roleplay Format
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+
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+ - Speech without quotes.
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+ - Actions in `*asterisks*`
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+
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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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+
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+ ### ST Settings
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+
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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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+
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+ ### Method
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+
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+ Finetuned using an A100 on Google Colab.
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+
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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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+
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+ ### Configuration
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+
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+ LoRA, model, and training settings:
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+
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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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+
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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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+
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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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+
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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",
100
+ )
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+
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+ # Create DPO trainer
103
+ dpo_trainer = DPOTrainer(
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+ model,
105
+ ref_model,
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+ args=training_args,
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+ train_dataset=dataset,
108
+ tokenizer=tokenizer,
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+ peft_config=peft_config,
110
+ beta=0.1,
111
+ force_use_ref_model=True
112
+ )
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+
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+ # Fine-tune model with DPO
115
+ dpo_trainer.train()
116
+ ```
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+ "<|system|>": 32006,
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+ "<|user|>": 32010
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+ }
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+ {
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+ "content": "<|user|>",
111
+ "lstrip": false,
112
+ "normalized": false,
113
+ "rstrip": true,
114
+ "single_word": false,
115
+ "special": true
116
+ }
117
+ },
118
+ "bos_token": "<s>",
119
+ "chat_template": "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
120
+ "clean_up_tokenization_spaces": false,
121
+ "eos_token": "<|endoftext|>",
122
+ "model_max_length": 131072,
123
+ "pad_token": "<|endoftext|>",
124
+ "padding_side": "left",
125
+ "sp_model_kwargs": {},
126
+ "tokenizer_class": "LlamaTokenizer",
127
+ "unk_token": "<unk>",
128
+ "use_default_system_prompt": false
129
+ }