Instructions to use ecnu-icalk/educhat-base-002-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ecnu-icalk/educhat-base-002-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ecnu-icalk/educhat-base-002-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ecnu-icalk/educhat-base-002-7b")
model = AutoModelForCausalLM.from_pretrained("ecnu-icalk/educhat-base-002-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ecnu-icalk/educhat-base-002-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ecnu-icalk/educhat-base-002-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ecnu-icalk/educhat-base-002-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ecnu-icalk/educhat-base-002-7b

SGLang

How to use ecnu-icalk/educhat-base-002-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ecnu-icalk/educhat-base-002-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ecnu-icalk/educhat-base-002-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ecnu-icalk/educhat-base-002-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ecnu-icalk/educhat-base-002-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ecnu-icalk/educhat-base-002-7b with Docker Model Runner:
```
docker model run hf.co/ecnu-icalk/educhat-base-002-7b
```

使用方法

本模型使用educhat-sft-002-data-osm数据，从BELLE训练得到，由于LLaMA对其衍生模型的限制，发布的权重只能包含差异部分，使用前请先按照以下流程转换权重。

Step1:将原始LLaMA权重转换为huggingface版本。

首先下载LLaMA原始权重，然后使用权重转换脚本转换权重。

python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/LLaMA_hf/7B

Step2:使用解密脚本将增量权重加到原始LLaMA权重上。

python ./decrypt.py --base /path/to/LLAMA_hf/7B --target ./educhat-base-002-7b-decrypt --delta /path/to/educhat-base-002-7b

使用示例

转换权重后，使用示例请参考：https://github.com/icalk-nlp/EduChat#%E4%BD%BF%E7%94%A8%E7%A4%BA%E4%BE%8B

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ecnu-icalk
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educhat-base-002-7b

使用方法

Step1:将原始LLaMA权重转换为huggingface版本。

Step2:使用解密脚本将增量权重加到原始LLaMA权重上。

使用示例

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