Instructions to use LL-Square/LLSquare-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LL-Square/LLSquare-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="LL-Square/LLSquare-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LL-Square/LLSquare-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("LL-Square/LLSquare-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LL-Square/LLSquare-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LL-Square/LLSquare-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LL-Square/LLSquare-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LL-Square/LLSquare-7B-Instruct

SGLang

How to use LL-Square/LLSquare-7B-Instruct 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 "LL-Square/LLSquare-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LL-Square/LLSquare-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LL-Square/LLSquare-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LL-Square/LLSquare-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LL-Square/LLSquare-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/LL-Square/LLSquare-7B-Instruct
```

LLSquare-7B-Instruct / README.md

lazarus19

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	---
	license: apache-2.0
	license_link: https://huggingface.co/LL-Square/LLSquare-7B-Instruct
	language:
	- en
	pipeline_tag: text-generation
	base_model: LL-Square/LLSquare-7B-Instruct-7B
	tags:
	- chat
	library_name: transformers
	---

	# LLSquare-7B-Instruct
	<a href="https://llsquare.vercel.app" target="_blank" style="margin: 2px;">
	<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20LLSquare%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
	</a>

	## Introduction

	LLSquare-7B-Instruct is the base model of LL-Square large language models. For LLSquare-7B-Instruct, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. LLSquare-7B-Instruct brings the following improvements upon LL-Square2:

	- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
	- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
	- Long-context Support up to 128K tokens and can generate up to 8K tokens.
	- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

	This repo contains the instruction-tuned 7B LLSquare-7B-Instruct model, which has the following features:
	- Type: Causal Language Models
	- Training Stage: Pretraining & Post-training
	- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
	- Number of Parameters: 7.61B
	- Number of Paramaters (Non-Embedding): 6.53B
	- Number of Layers: 28
	- Number of Attention Heads (GQA): 28 for Q and 4 for KV
	- Context Length: Full 131,072 tokens and generation 8192 tokens
	- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy LLSquare-7B-Instruct for handling long texts.


	## Requirements

	The code of LLSquare-7B-Instruct has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

	With `transformers<4.37.0`, you will encounter the following error:
	```
	KeyError: 'LL-Square2'
	```

	## Quickstart

	Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "LL-Square/LLSquare-7B-Instruct-7B-Instruct"

	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	prompt = "Give me a short introduction to large language model."
	messages = [
	{"role": "system", "content": "You are LL-Square, created by Alibaba Cloud. You are a helpful assistant."},
	{"role": "user", "content": prompt}
	]
	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)
	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=512
	)
	generated_ids = [
	output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
	]

	response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
	```

	### Processing Long Texts

	The current `config.json` is set for context length up to 32,768 tokens.
	To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

	For supported frameworks, you could add the following to `config.json` to enable YaRN:
	```json
	{
	...,
	"rope_scaling": {
	"factor": 4.0,
	"original_max_position_embeddings": 32768,
	"type": "yarn"
	}
	}
	```