QuantFactory/internlm2-chat-1_8b-GGUF

This is quantized version of internlm/internlm2-chat-1_8b created using llama.cpp

Model Description

 

InternLM ^HOT

 

Introduction

InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has three versions of open-source models. They are:

• InternLM2-1.8B: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
• InternLM2-Chat-1.8B-SFT: Chat model after supervised fine-tuning (SFT) on InternLM2-1.8B.
• InternLM2-Chat-1.8B: Further aligned on top of InternLM2-Chat-1.8B-SFT through online RLHF. InternLM2-Chat-1.8B exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.

The InternLM2 has the following technical features:

• Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
• Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.

InternLM2-1.8B

Performance Evaluation

We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool OpenCompass. Some of the evaluation results are shown in the table below. You are welcome to visit the OpenCompass Leaderboard for more evaluation results.

Dataset\Models	InternLM2-1.8B	InternLM2-Chat-1.8B-SFT	InternLM2-7B	InternLM2-Chat-7B
MMLU	46.9	47.1	65.8	63.7
AGIEval	33.4	38.8	49.9	47.2
BBH	37.5	35.2	65.0	61.2
GSM8K	31.2	39.7	70.8	70.7
MATH	5.6	11.8	20.2	23.0
HumanEval	25.0	32.9	43.3	59.8
MBPP(Sanitized)	22.2	23.2	51.8	51.4

• The evaluation results were obtained from OpenCompass , and evaluation configuration can be found in the configuration files provided by OpenCompass.
• The evaluation data may have numerical differences due to the version iteration of OpenCompass, so please refer to the latest evaluation results of OpenCompass.

Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.

Import from Transformers

To load the InternLM2 1.8B Chat model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-1_8b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-1_8b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)
# Hello! How can I help you today?
response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
print(response)

The responses can be streamed using stream_chat:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "internlm/internlm2-chat-1_8b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "Hello", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

Deployment

LMDeploy

LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.

pip install lmdeploy

You can run batch inference locally with the following python code:

import lmdeploy
pipe = lmdeploy.pipeline("internlm/internlm2-chat-1_8b")
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)

Or you can launch an OpenAI compatible server with the following command:

lmdeploy serve api_server internlm/internlm2-chat-1_8b --model-name internlm2-chat-1_8b --server-port 23333 

Then you can send a chat request to the server:

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-1_8b",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Introduce deep learning to me."}
    ]
    }'

Find more details in the LMDeploy documentation

vLLM

Launch OpenAI compatible server with vLLM>=0.3.2:

pip install vllm

python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-1_8b --served-model-name internlm2-chat-1_8b --trust-remote-code

Then you can send a chat request to the server:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-1_8b",
    "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Introduce deep learning to me."}
    ]
    }'

Find more details in the vLLM documentation

Open Source License

The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact [email protected].