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HomeLLMsdeepseek llm 7b chat

deepseek llm 7b chat

by deepseek-ai

Open source · 184k downloads · 219 likes

2.9
(219 reviews)ChatAPI & Local
About

DeepSeek LLM 7B Chat is an advanced language model with 7 billion parameters, specifically designed to understand and generate text in both English and Chinese. Fine-tuned from a base model, it excels in dialogue and instruction-following tasks, delivering precise and contextually relevant responses. Its primary use cases include conversational assistance, content generation, text data analysis, and the automation of complex linguistic tasks. The model stands out for its ability to handle diverse queries while maintaining high coherence, making it suitable for both developers and businesses seeking to integrate a high-performance and versatile AI.

Documentation

DeepSeek Chat

[🏠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].

Capabilities & Tags
transformerspytorchllamatext-generationconversationaltext-generation-inferenceendpoints_compatible
Links & Resources
Specifications
CategoryChat
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Parameters7B parameters
Rating
2.9

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