zhanxu/EcosilkChat-7b

EcoSilkModel

Model Overview

EcoSilkModel is a fine-tuned language model based on DeepSeek-LLM-7B, specifically designed for sustainable agriculture, silk production, and eco-friendly practices. The model excels in the following tasks:

• Silk Production Guidance: Provides recommendations for sustainable silk farming and sericulture.
• Eco-friendly Practices: Offers suggestions for environmentally friendly agricultural practices.
• Chinese Language Support: Focuses on Chinese tasks while also supporting both Chinese and English.

This model is part of the EcoSilk Project, aimed at promoting sustainable development in the silk industry.

Applicable Scenarios

The model is suitable for the following scenarios:

• Researchers: Studying sustainable agriculture and silk production.
• Farmers: Seeking guidance on eco-friendly agricultural practices.
• Educators: Teaching sustainable agricultural practices.
• Developers: Building applications for the silk and agricultural industries.

Training Data

The model was fine-tuned on the following datasets:

1. zhanxu/ecosilk-chat: A Chinese instruction-following dataset used for fine-tuning language models.（https://huggingface.co/datasets/zhanxu/ecosilk-chat） The dataset is derived from professional literature across various fields. It uses open-source tools to automatically annotate question-answer datasets, which are then manually cleaned and filtered. For more details, see GitHub - ConardLi/easy-dataset: A powerful tool for creating fine-tuning datasets for LLM.

Fine-tuning Details

• Base Model: DeepSeek-LLM-7B
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Template: llama3
• Languages: Chinese (zh) and English (en)
• Truncation Length: 1024 tokens

How to Use Our Model

Here are some examples of how to use our model.

Chat Completion

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)