Parameter	Value
Base Model	`Qwen/Qwen3.5-9B`
Trainer	`sft`
Dataset	`monostate/hotel-quotation-syntv4-text`
Epochs	3
Learning Rate	0.0005
Batch Size	1
Block Size	16384
LoRA Rank	32
LoRA Alpha	64
Gradient Accumulation	16

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

print(response)

Trained with AITraining

andrewmonostate/sophia-quotation-v12-sft

sophia-quotation-v12-sft

Training Details

Usage

andrewmonostate/
sophia-quotation-v12-sft