flammenai
/

flammen15-gutenberg-DPO-v1-7B

@@ -1,201 +1,83 @@
 ---
 library_name: transformers
 license: apache-2.0
 datasets:
 - jondurbin/gutenberg-dpo-v0.1
 ---
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 ---
 library_name: transformers
 license: apache-2.0
+base_model:
+  - nbeerbower/flammen15-mistral-7B
 datasets:
 - jondurbin/gutenberg-dpo-v0.1
 ---
+![image/png](https://huggingface.co/nbeerbower/flammen13X-mistral-7B/resolve/main/flammen13x.png)
+# flammen15-gutenberg-DPO-v1-7B
+A Mistral 7B LLM built from merging pretrained models and finetuning on Jon Durbin's Gutenberg DPO set. 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
+### 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=2,
+    gradient_accumulation_steps=2,
+    gradient_checkpointing=True,
+    learning_rate=2e-5,
+    lr_scheduler_type="cosine",
+    max_steps=200,
+    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=1024,
+    max_length=1536,
+    force_use_ref_model=True
+)
+# Fine-tune model with DPO
+dpo_trainer.train()
+```