Instructions to use ZachW/CollabLLM-Qwen3-4B-SFT-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ZachW/CollabLLM-Qwen3-4B-SFT-Math with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B") model = PeftModel.from_pretrained(base_model, "ZachW/CollabLLM-Qwen3-4B-SFT-Math") - Notebooks
- Google Colab
- Kaggle
File size: 1,525 Bytes
becd523 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"base_model": "Qwen/Qwen3-4B",
"base_model_revision": "1cfa9a7208912126459214e8b04321603b3df60c",
"task": "math-hard",
"method": "supervised fine-tuning (LoRA)",
"enable_thinking": false,
"training_conversations": 450,
"validation_conversations": 50,
"global_step": 24,
"epochs_completed": 3.0,
"data_source": "https://github.com/Wuyxin/collabllm/tree/d573e4949abdc301cb2b630525c593cc432e94b8/data/interactive-math-hard",
"data_preparation": "For each dialogue idx, select the row with the longest prompt and append its chosen assistant response. Hold out 50 of 500 conversations using seed 42.",
"adapter_sha256": "ac1e634d0ab09341a38a4be7e54959edccf94509fffd6d448d7ad7f52c1b0e9e",
"hyperparameters": {
"seed": 42,
"learning_rate": 1e-05,
"warmup_ratio": 0.1,
"max_seq_length": 1536,
"num_train_epochs": 3,
"per_device_train_batch_size": 1,
"gradient_accumulation_steps": 64,
"assistant_only_loss": true,
"peft_r": 32,
"peft_alpha": 16,
"peft_dropout": 0.1,
"target_modules": "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
"use_4bit": false
},
"training_metrics": {
"total_flos": 4.277110198134374e+16,
"train_loss": 0.8463213642438253,
"train_runtime": 1324.9645,
"train_samples_per_second": 1.019,
"train_steps_per_second": 0.018
},
"validation_metrics": {
"eval_loss": 0.7929708361625671,
"eval_runtime": 13.7847,
"eval_samples_per_second": 3.627,
"eval_steps_per_second": 3.627
}
}
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