Internlm 7b

🚀 InternLM

InternLM has open-sourced a 7 billion parameter base model tailored for practical scenarios, leveraging trillions of high - quality tokens for training and providing a versatile toolset for users.

🚀 Quick Start

If you want to use the InternLM-7B model, you can load it through the Transformers library. Here is a simple example:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
inputs = tokenizer(["A beautiful flower"], return_tensors="pt")
for k,v in inputs.items():
    inputs[k] = v.cuda()
gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
output = model.generate(**inputs, **gen_kwargs)
output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
print(output)
# <s> A beautiful flower box made of white rose wood. It is a perfect gift for weddings, birthdays and anniversaries.
# All the roses are from our farm Roses Flanders. Therefor you know that these flowers last much longer than those in store or online!</s>

✨ Features

InternLM has the following features:

• It leverages trillions of high - quality tokens for training to establish a powerful knowledge base.
• It provides a versatile toolset for users to flexibly build their own workflows.

📚 Documentation

InternLM-7B

Performance Evaluation

We conducted a comprehensive evaluation of InternLM using the open - source evaluation tool OpenCompass. The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the OpenCompass leaderboard for more evaluation results.

• The evaluation results were obtained from OpenCompass 20230706 (some data marked with *, which means come from the original papers), 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.

⚠️ Important Note

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 InternLM 7B Chat model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
inputs = tokenizer(["A beautiful flower"], return_tensors="pt")
for k,v in inputs.items():
    inputs[k] = v.cuda()
gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
output = model.generate(**inputs, **gen_kwargs)
output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
print(output)
# <s> A beautiful flower box made of white rose wood. It is a perfect gift for weddings, birthdays and anniversaries.
# All the roses are from our farm Roses Flanders. Therefor you know that these flowers last much longer than those in store or online!</s>

📄 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 internlm@pjlab.org.cn.