Instructions to use ram-lexsi/agenttune-testrun-vector-memory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram-lexsi/agenttune-testrun-vector-memory with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram-lexsi/agenttune-testrun-vector-memory", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,105 Bytes
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"model": "HuggingFaceTB/SmolLM2-360M-Instruct",
"dataset_name": "unknown",
"output_dir": "./out_vector-memory",
"agentic_mode": true,
"tools": [
"search_kb"
],
"train_size": 40,
"eval_size": 0,
"final_loss": -0.1025804548036831,
"total_steps": 10,
"training_history": [],
"metrics": {
"train_runtime": 252.8961,
"train_samples_per_second": 0.158,
"train_steps_per_second": 0.04,
"total_flos": 0.0,
"train_loss": -0.1025804548036831,
"epoch": 1.0
},
"config_kwargs": {
"model": "HuggingFaceTB/SmolLM2-360M-Instruct",
"train_dataset": "Dataset({\n features: ['prompt', 'answer'],\n num_rows: 40\n})",
"reward_funcs": "<callable: combined_reward(reward_tool_used+reward_correct_answer)>",
"tools": [
"<function search_kb at 0x79adf0a83ec0>"
],
"system_prompt": "You are the Dallas DC policy recall agent. Reply with the short answer only.",
"output_dir": "./out_vector-memory",
"num_train_epochs": 1,
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 4,
"learning_rate": 1e-06,
"warmup_ratio": 0.03,
"warmup_steps": 10,
"weight_decay": 0.0,
"lr_scheduler_type": "linear",
"max_grad_norm": 1.0,
"seed": 42,
"data_seed": 42,
"optim": "adamw_torch",
"num_generations": 2,
"max_prompt_length": 256,
"max_completion_length": 128,
"temperature": 0.7,
"top_p": 0.95,
"beta": 0.04,
"use_vllm": false,
"vllm_mode": "colocate",
"log_completions": true,
"chat_template_kwargs": {
"enable_thinking": false
},
"max_steps_per_turn": 8,
"rollout_backend": null,
"engine_kwargs": {},
"peft_config": {
"r": 8,
"lora_alpha": 16,
"lora_dropout": 0.0,
"bias": "none",
"task_type": "CAUSAL_LM"
},
"bf16": true,
"fp16": false,
"gradient_checkpointing": true,
"dataloader_num_workers": 0,
"logging_steps": 1,
"save_steps": 500,
"report_to": "none",
"push_to_hub": false,
"eval_strategy": "no",
"remove_unused_columns": false
}
} |