deepseek llm 7b chat
par deepseek-ai
Open source · 183k downloads · 219 likes
2.9
(219 avis)ChatAPI & LocalÀ propos
DeepSeek LLM 7B Chat est un modèle de langage avancé doté de 7 milliards de paramètres, spécialement conçu pour comprendre et générer du texte en anglais et en chinois. Fine-tuné à partir d’un modèle de base, il excelle dans les tâches de dialogue et de suivi d’instructions, offrant des réponses précises et contextuelles. Ses principaux cas d’usage incluent l’assistance conversationnelle, la génération de contenu, l’analyse de données textuelles et l’automatisation de tâches linguistiques complexes. Ce modèle se distingue par sa capacité à traiter des requêtes variées tout en maintenant une cohérence élevée, le rendant adapté aussi bien aux développeurs qu’aux entreprises cherchant à intégrer une IA performante et polyvalente.
Documentation
[🏠Homepage] | [🤖 Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)]
1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
2. Model Summary
deepseek-llm-7b-chat is a 7B parameter model initialized from deepseek-llm-7b-base and fine-tuned on extra instruction data.
- Home Page: DeepSeek
- Repository: deepseek-ai/deepseek-LLM
- Chat With DeepSeek LLM: DeepSeek-LLM
3. How to Use
Here give some examples of how to use our model.
Chat Completion
Python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
Avoiding the use of the provided function apply_chat_template, you can also interact with our model following the sample template. Note that messages should be replaced by your input.
CSS
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
Note: By default (add_special_tokens=True), our tokenizer automatically adds a bos_token (<｜begin▁of▁sentence｜>) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the LICENSE-MODEL for more details.
5. Contact
If you have any questions, please raise an issue or contact us at [email protected].
Liens & Ressources