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MM-AdaptionLabs
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Model Details
Model Provider
MM-AdaptionLabs
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
A LORA fine-tune of Qwen/Qwen3.5-0.8B. This model was trained with supervised fine-tuning (SFT) using Adaption's AutoScientist on the general_knowledge_qa dataset.
json
{"finetune_job_id": "69d2d2ca-5984-45d7-8e07-f24a59d39424","training_experiment_id": "b3805568-3565-4d1c-ad72-4e8173aee82f","original_model_name": "Qwen/Qwen3.5-0.8B","trained_model_name": "adaption_general_knowledge_qa","training_method": "sft","training_type": "lora","data_format": "chat","hyperparams": {"lora": "true","lora_r": 16,"n_evals": 5,"n_epochs": 1,"batch_size": "max","lora_alpha": 32,"lora_dropout": 0,"min_lr_ratio": 0.1,"warmup_ratio": 0.03,"weight_decay": 0,"learning_rate": 0.00001,"max_grad_norm": 2,"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"}}
The model was fine-tuned on 3,588 rows of adapted data with the following domain distribution: history (11%), sports (10%), science (9%), geography (7%), entertainment (6%), music (6%), animal-nature (5%), cooking (5%), culture (4%), travel (3%), transportation (3%), technology (2%), corporate-business (2%), governance (2%), medical (2%), games (2%), writing-editing-communication (2%), fitness-sports (2%), academic-education (1%), math (1%), code (1%), language (1%), agriculture (1%), how-to (1%), personal-finance (1%), art (1%), career-workplace (1%), religion (1%), product-advice (1%), parenting-family (1%), personal-growth (1%), fashion-beauty (1%), marketing (0%), architecture-design (0%), data-analysis-visualization (0%), other (0%), legal (0%), dating (0%), market-analysis (0%), news (0%), hr (0%), social (0%), roleplay (0%), literature (0%).
The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.
bash
pip install torch transformers peft
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelBASE = "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.bfloat16base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)model = PeftModel.from_pretrained(base, ADAPTER)# Optional: merge the LoRA weights into the base for faster inferencemodel = 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))