Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+base_model:
+- flammenai/flammen22-mistral-7B
+datasets:
+- flammenai/casual-conversation-DPO
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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+![image/png](https://huggingface.co/nbeerbower/flammen13X-mistral-7B/resolve/main/flammen13x.png)
+
+# flammen22C-mistral-7B
+
+A Mistral 7B LLM built from merging pretrained models and finetuning on [flammenai/casual-conversation-DPO](https://huggingface.co/datasets/flammenai/casual-conversation-DPO). 
+Flammen specializes in exceptional character roleplay, creative writing, and general intelligence
+
+### 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
+
+System prompt, dataset formatting:
+
+```python
+def chatml_format(example):
+    # Initialize formatted system message
+    system = ""
+    message = {"role": "system", "content": "You are an AI character talking to a human. Engage in casual conversation."}
+    system = tokenizer.apply_chat_template([message], tokenize=False)
+
+    # Format instruction
+    message = {"role": "user", "content": example['prompt']}
+    prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True)
+
+    # Format chosen answer
+    chosen = example['chosen'] + "<|im_end|>\n"
+
+    # Format rejected answer
+    rejected = example['rejected'] + "<|im_end|>\n"
+
+    return {
+        "prompt": system + prompt,
+        "chosen": chosen,
+        "rejected": rejected,
+    }
+
+dataset = load_dataset("flammenai/casual-conversation-DPO")['train']
+
+# Save columns
+original_columns = dataset.column_names
+
+# Tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "left"
+
+# Format dataset
+dataset = dataset.map(
+    chatml_format,
+    remove_columns=original_columns
+)
+```
+
+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,
+    max_prompt_length=2048,
+    max_length=4096,
+    force_use_ref_model=True
+)
+
+# Fine-tune model with DPO
+dpo_trainer.train()
+```