README.md

metadata

license: other
language:
  - en
pipeline_tag: text-generation
inference: false
tags:
  - transformers
  - gguf
  - imatrix
  - LongWriter-llama3.1-8b

Quantizations of https://huggingface.co/THUDM/LongWriter-llama3.1-8b

Inference Clients/UIs

• llama.cpp
• KoboldCPP
• text-generation-webui
• ollama

---

From original readme

LongWriter-llama3.1-8b is trained based on Meta-Llama-3.1-8B, and is capable of generating 10,000+ words at once.

Environment: transformers>=4.43.0

Please ahere to the prompt template (system prompt is optional): <<SYS>>\n{system prompt}\n<</SYS>>\n\n[INST]{query1}[/INST]{response1}[INST]{query2}[/INST]{response2}...

A simple demo for deployment of the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-llama3.1-8b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-llama3.1-8b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
model = model.eval()
query = "Write a 10000-word China travel guide"
prompt = f"[INST]{query}[/INST]"
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
context_length = input.input_ids.shape[-1]
output = model.generate(
    **input,
    max_new_tokens=32768,
    num_beams=1,
    do_sample=True,
    temperature=0.5,
)[0]
response = tokenizer.decode(output[context_length:], skip_special_tokens=True)
print(response)

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

model = LLM(
    model= "THUDM/LongWriter-llama3.1-8b",
    dtype="auto",
    trust_remote_code=True,
    tensor_parallel_size=1,
    max_model_len=32768,
    gpu_memory_utilization=0.5,
)
tokenizer = model.get_tokenizer()
generation_params = SamplingParams(
    temperature=0.5,
    top_p=0.8,
    top_k=50,
    max_tokens=32768,
    repetition_penalty=1,
)
query = "Write a 10000-word China travel guide"
prompt = f"[INST]{query}[/INST]"
input_ids = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0].tolist()
outputs = model.generate(
    sampling_params=generation_params,
    prompt_token_ids=[input_ids],
)
output = outputs[0]
print(output.outputs[0].text)