This is a finetune of InfinityNexus_9B. This is my first time tuning a frankenmerge, so hopefully it works out. The goal is to improve intelligence and RP ability beyond the 7B original models.
1from awq import AutoAWQForCausalLM
2from transformers import AutoTokenizer, TextStreamer
3
4model_path = "solidrust/Garbage_9B-AWQ"
5system_message = "You are Garbage_9B, incarnated as a powerful AI. You were created by jeiku."
6
7# Load model
8model = AutoAWQForCausalLM.from_quantized(model_path,
9 fuse_layers=True)
10tokenizer = AutoTokenizer.from_pretrained(model_path,
11 trust_remote_code=True)
12streamer = TextStreamer(tokenizer,
13 skip_prompt=True,
14 skip_special_tokens=True)
15
16# Convert prompt to tokens
17prompt_template = """\
18<|im_start|>system
19{system_message}<|im_end|>
20<|im_start|>user
21{prompt}<|im_end|>
22<|im_start|>assistant"""
23
24prompt = "You're standing on the surface of the Earth. "\
25 "You walk one mile south, one mile west and one mile north. "\
26 "You end up exactly where you started. Where are you?"
27
28tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
29 return_tensors='pt').input_ids.cuda()
30
31# Generate output
32generation_output = model.generate(tokens,
33 streamer=streamer,
34 max_new_tokens=512)
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
