from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-glm4-9b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-glm4-9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
model = model.eval()
query = "Write a 10000-word China travel guide"
response, history = model.chat(tokenizer, query, history=[], max_new_tokens=32768, temperature=0.5)
print(response)

You can also deploy the model with vllm, which allows 10,000+ words generation within a minute. Here is an example code:

python

from vllm import LLM, SamplingParams
model = LLM(
    model= "THUDM/LongWriter-glm4-9b",
    dtype="auto",
    trust_remote_code=True,
    tensor_parallel_size=1,
    max_model_len=32768,
    gpu_memory_utilization=1,
)
tokenizer = model.get_tokenizer()
stop_token_ids = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), tokenizer.get_command("<|observation|>")]
generation_params = SamplingParams(
    temperature=0.5,
    top_p=0.8,
    top_k=50,
    max_tokens=32768,
    repetition_penalty=1,
    stop_token_ids=stop_token_ids
)
query = "Write a 10000-word China travel guide"
input_ids = tokenizer.build_chat_input(query, history=[], role='user').input_ids[0].tolist()
outputs = model.generate(
    sampling_params=generation_params,
    prompt_token_ids=[input_ids],
)
output = outputs[0]
print(output.outputs[0].text)

duyntnet/LongWriter-glm4-9b-imatrix-GGUF

Inference Clients/UIs

From original readme

duyntnet/
LongWriter-glm4-9b-imatrix-GGUF