1. Base Model Deepseek LLM
The base model is Deepseek-7b-chat model , lora dataset is aaming77/huanhuan · Datasets at Hugging Face
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.
model details you can see their Repository: deepseek-ai/deepseek-LLM
3. How to Use
Here give some examples of how to use our model.
Chat Completion
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "libraxiong/deepseek-zhenhuan-lora"
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.
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. Acknowledgement
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