Spaces:
Running on Zero
Running on Zero
File size: 1,768 Bytes
79e76a6 9730eba 79e76a6 | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
)
MODEL_ID = "dphn/dolphin-2.9.4-llama3.1-8b"
config = AutoConfig.from_pretrained(MODEL_ID)
print("model_type:", config.model_type)
print("hidden_size:", getattr(config, "hidden_size", "unknown"))
print("num_hidden_layers:", getattr(config, "num_hidden_layers", "unknown"))
print("num_attention_heads:", getattr(config, "num_attention_heads", "unknown"))
print("num_key_value_heads:", getattr(config, "num_key_value_heads", "unknown"))
tokenizer = AutoTokenizer.from_pretrained(
MODEL_ID,
padding_side="left",
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
config=config,
torch_dtype="auto",
device_map="auto",
)
generation_config = GenerationConfig.from_pretrained(MODEL_ID)
generation_config.max_new_tokens = 120
generation_config.do_sample = True
generation_config.temperature = 0.7
generation_config.top_p = 0.9
generation_config.repetition_penalty = 1.05
generation_config.pad_token_id = tokenizer.pad_token_id
generation_config.eos_token_id = tokenizer.eos_token_id
messages = [
{"role": "system", "content": "你是一个面试辅导老师。"},
{"role": "user", "content": "解释 prefill 和 decode 的区别。"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
generation_config=generation_config,
)
new_token_ids = outputs[0][inputs["input_ids"].shape[-1] :]
answer = tokenizer.decode(new_token_ids, skip_special_tokens=True)
print(answer)
|