flammenai
/

Mahou-1.2-phi-14B

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 ---
 library_name: transformers
-tags: []
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 ---
 library_name: transformers
+license: apache-2.0
+base_model:
+- microsoft/Phi-3-medium-128k-instruct
+datasets:
+- flammenai/FlameMix-DPO-v1
+- flammenai/Grill-preprod-v1_chatML
+- flammenai/Grill-preprod-v2_chatML
 ---
+![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png)
+# Mahou-1.2-phi-14B
+Please note: this is an untested, experimental release.
+Mahou is our attempt to build a production-ready conversational/roleplay LLM.
+Future versions will be released iteratively and finetuned from flammen.ai conversational data.
+### Chat Format
+This model has been trained to use ChatML format.
+```
+<|im_start|>system
+{{system}}<|im_end|>
+<|im_start|>{{char}}
+{{message}}<|im_end|>
+<|im_start|>{{user}}
+{{message}}<|im_end|>
+```
+# Roleplay Format
+- Speech without quotes.
+- Actions in `*asterisks*`
+```
+*leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass.
+```
+### ST Settings
+1. Use ChatML for the Context Template.
+2. Turn on Instruct Mode for ChatML.
+3. Use the following stopping strings: `["<", "|", "<|", "\n"]`
+### Method
+Finetuned using an A100 on Google Colab.
+[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)
+### Configuration
+LoRA, model, and training settings:
+```python
+# LoRA configuration
+peft_config = LoraConfig(
+    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']
+)
+# Model to fine-tune
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    load_in_4bit=True
+)
+model.config.use_cache = False
+# Reference model
+ref_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    load_in_4bit=True
+)
+# Training arguments
+training_args = TrainingArguments(
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    gradient_checkpointing=True,
+    learning_rate=5e-5,
+    lr_scheduler_type="cosine",
+    max_steps=2000,
+    save_strategy="no",
+    logging_steps=1,
+    output_dir=new_model,
+    optim="paged_adamw_32bit",
+    warmup_steps=100,
+    bf16=True,
+    report_to="wandb",
+)
+# Create DPO trainer
+dpo_trainer = DPOTrainer(
+    model,
+    ref_model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+    peft_config=peft_config,
+    beta=0.1,
+    force_use_ref_model=True
+)
+# Fine-tune model with DPO
+dpo_trainer.train()
+```