--- language: - en - zh library_name: transformers tags: - Long Context - chatglm - llama datasets: - THUDM/LongWriter-6k license: llama3.1 --- ![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ) # QuantFactory/LongWriter-llama3.1-8b-GGUF This is quantized version of [THUDM/LongWriter-llama3.1-8b](https://huggingface.co/THUDM/LongWriter-llama3.1-8b) created using llama.cpp # Original Model Card # LongWriter-llama3.1-8b
🤗 [LongWriter Dataset] • 💻 [Github Repo] • 📃 [LongWriter Paper]
LongWriter-llama3.1-8b is trained based on [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B), and is capable of generating 10,000+ words at once. A simple demo for deployment of the model: ```python 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) ``` Please ahere to the prompt template (system prompt is optional): `<