adaption_agent_memory

adaption_agent_memory

Model Training

A LORA adapter for Qwen/Qwen3.5-0.8B. This model was trained with SFT using Adaption's AutoScientist on the agent-memory dataset.

AutoScientist Config

{
  "job_id": "8a0baade-7640-4703-b702-463b150dd1c2",
  "training_experiment_id": "9909ea8f-7e49-4536-9642-aa39a6054e51",
  "original_model_name": "Qwen/Qwen3.5-0.8B",
  "trained_model_name": "adaption_agent_memory",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 32,
    "n_evals": 5,
    "n_epochs": 4,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0.05,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.01,
    "learning_rate": 0.00005,
    "max_grad_norm": 1,
    "base_model_size": "0.8B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"
  }
}

Training Data

The model was trained on 5,767 rows of adapted data with the following domain distribution: cooking (11%), academic-education (10%), math (9%), medical (9%), sports (9%), corporate-business (8%), entertainment (7%), code (7%), personal-finance (4%), fitness-sports (4%), writing-editing-communication (4%), personal-growth (3%), legal (3%), product-advice (3%), technology (3%), career-workplace (3%), parenting-family (2%), how-to (1%), marketing (1%), hr (1%), dating (1%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

How to use

pip install torch transformers peft

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE = "Qwen/Qwen3.5-0.8B"
ADAPTER = "<this-repo-id>"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16

base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()

tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))