[?Homepage] | [? Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)] Itroducig DeepSeek LLM, a advaced laguage model comprisig 67 billio parameters. It has bee traied from scratch o a vast dataset of 2 trillio tokes i both Eglish ad Chiese. I order to foster research, we have made DeepSeek LLM 7B/67B Base ad DeepSeek LLM 7B/67B Chat ope source for the research commuity. Here give some examples of how to use our model. This code repository is licesed uder the MIT Licese. The use of DeepSeek LLM models is subject to the Model Licese. DeepSeek LLM supports commercial use. See the LICENSE-MODEL for more details. If you have ay questios, please raise a issue or cotact us at service@deepseek.com.
1. Itroductio of Deepseek LLM
2. Model Summary
deepseek-llm-67b-base
is a 67B parameter model with Grouped-Query Attetio traied o 2 trillio tokes from scratch.
3. How to Use
Text Completio
import torch
from modelscope import AutoTokeizer, AutoModelForCausalLM, GeeratioCofig
model_ame = "deepseek-ai/deepseek-llm-67b-base"
tokeizer = AutoTokeizer.from_pretraied(model_ame)
model = AutoModelForCausalLM.from_pretraied(model_ame, torch_dtype=torch.bfloat16, device_map="auto")
model.geeratio_cofig = GeeratioCofig.from_pretraied(model_ame)
model.geeratio_cofig.pad_toke_id = model.geeratio_cofig.eos_toke_id
text = "A attetio fuctio ca be described as mappig a query ad a set of key-value pairs to a output, where the query, keys, values, ad output are all vectors. The output is"
iputs = tokeizer(text, retur_tesors="pt")
outputs = model.geerate(**iputs.to(model.device), max_ew_tokes=100)
result = tokeizer.decode(outputs[0], skip_special_tokes=True)
prit(result)
4. Licese
5. Cotact
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