Instructions to use brikdavies/qwen1.7B-MMLU-hint-following-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brikdavies/qwen1.7B-MMLU-hint-following-RL with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("brikdavies/qwen1.7B-MMLU-hint-following-RL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,670 Bytes
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"model_name": "Qwen/Qwen3-1.7B",
"init_checkpoint": "",
"ref_model_name": "",
"skip_first_n_prompts": 0,
"dataset_name": "cais/mmlu",
"mmlu_config": "all",
"mmlu_split": "test",
"lambda_length": 0.0,
"format_penalty": -1.0,
"max_steps": 250,
"per_device_train_batch_size": 0,
"gradient_accumulation_steps": 6,
"learning_rate": 5e-05,
"warmup_ratio": 0.05,
"lr_scheduler_type": "constant_with_warmup",
"max_grad_norm": 1.0,
"bf16": true,
"gradient_checkpointing": true,
"num_generations": 6,
"max_completion_length": 3000,
"max_prompt_length": 512,
"temperature": 0.6,
"top_p": 0.95,
"loss_type": "dr_grpo",
"beta": 0.0,
"num_iterations": 1,
"epsilon": 0.2,
"scale_rewards": "group",
"lora_rank": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
],
"use_vllm": true,
"vllm_mode": "colocate",
"vllm_gpu_memory_utilization": 0.2,
"vllm_model_impl": "transformers",
"logging_steps": 1,
"log_completions": false,
"report_to": "wandb",
"save_steps": 50,
"save_total_limit": 5,
"output_dir": "./outputs",
"run_name": "hint_follow_lr5e5_250steps",
"seed": 42,
"baseline_num_problems": 100,
"baseline_generations_per_problem": 8,
"wandb_project": "grpo-mmlu-hint",
"lora_config": {
"r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"target_modules": [
"v_proj",
"o_proj",
"q_proj",
"gate_proj",
"k_proj",
"down_proj",
"up_proj"
],
"bias": "none",
"task_type": "TaskType.CAUSAL_LM"
}
} |