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License: apache-2.0Output format
markdown
<think>[3-5 sentences of persona-grounded reasoning]</think>{"choice": "A", "confidence": 0.82, "reasoning": "one sentence"}
choice is always A (Disagree/No), B (Mixed/Neutral), or C (Agree/Yes).
Training details
| Setting | Value |
|---|---|
| Base model | Qwen/Qwen2.5-14B-Instruct |
| Training data | SocSci210 6k (fixed) |
| Method | QLoRA 4-bit, NF4, double quant |
| LoRA r / alpha | 64 / 128 |
| LoRA targets | q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj |
| Effective batch | 64 (8 × 8 grad accum) |
| Epochs | 2 |
| Learning rate | 0.0002 (cosine schedule) |
| Max seq length | 2048 |
| Training time | 1.21 hrs |
| Training cost | $1.69 |
| Resumed from | basab1142/sft-14b-v1 |
Usage
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torchbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct",quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16),device_map="auto",)model = PeftModel.from_pretrained(base, "basab1142/sft-14b-v1")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
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