Neural Krishna DPO

Fine-tuning + lnegth(choose)

  • Training Args:
# 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.float16,
    load_in_4bit=True
)
model.config.use_cache = False



# 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=120,
    save_strategy="no",
    logging_steps=1,
    output_dir=new_model,
    optim="paged_adamw_32bit",
    warmup_steps=50,
    bf16=True,
    report_to="wandb",
)

# Create DPO trainer
dpo_trainer = DPOTrainer(
    model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    peft_config=peft_config,
    beta=0.1,
    max_prompt_length=1024,
    max_length=1536,
)

# Fine-tune model with DPO
dpo_trainer.train()

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 76.00
AI2 Reasoning Challenge (25-Shot) 74.06
HellaSwag (10-Shot) 88.97
MMLU (5-Shot) 64.41
TruthfulQA (0-shot) 76.19
Winogrande (5-shot) 84.29
GSM8k (5-shot) 68.08
Downloads last month
887
Safetensors
Model size
7.24B params
Tensor type
FP16
Β·
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.

Model tree for Kukedlc/NeuralKrishna-7B-V2-DPO

Merges
2 models
Quantizations
3 models

Spaces using Kukedlc/NeuralKrishna-7B-V2-DPO 6

Evaluation results