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Charley890
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Model Details
Model Provider
Charley890
Model Tree
meta-llama/Llama-4-Scout-17B-16E-Instruct
Input Modalities
Output Modalities
Supported Functionality
A LORA adapter for meta-llama/Llama-4-Scout-17B-16E-Instruct. This model was trained with SFT using Adaption's AutoScientist on the floating_point_values dataset.
json
{"job_id": "a8c1d73d-b54f-4441-a689-00ba4fe72428","training_experiment_id": "2e0ac1f6-4559-4728-9c17-0e4e60797ecb","original_model_name": "meta-llama/Llama-4-Scout-17B-16E-Instruct","trained_model_name": "adaption_floating_point_values","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": 0.00005,"max_grad_norm": 2,"base_model_size": "109B","train_on_inputs": "false","training_method": "sft","lr_scheduler_type": "cosine","scheduler_num_cycles": 0.5,"lora_trainable_modules": "k_proj,o_proj,q_proj,v_proj,shared_expert.gate_proj,shared_expert.up_proj,shared_expert.down_proj,feed_forward.gate_proj,feed_forward.up_proj,feed_forward.down_proj"}}
The model was trained on 1,348 rows of adapted data with the following domain distribution: math (73%), science (24%), code (3%), corporate-business (0%), data-analysis-visualization (0%), music (0%), technology (0%), personal-finance (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.
| Domain | Win rate vs. base model |
|---|---|
| math | 58% |
bash
pip install torch transformers peft
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelBASE = "meta-llama/Llama-4-Scout-17B-16E-Instruct"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))