Infinity-Instruct-7M-Gen-Llama3.1-70B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on Infinity-Instruct-7M and Infinity-Instruct-Gen and showing favorable results on AlpacaEval 2.0 and arena-hard compared to GPT4.

News

🔥🔥🔥[2024/08/02] We release the model weights of InfInstruct-Llama3.1-70B Gen, InfInstruct-Llama3.1-8B Gen, InfInstruct-Mistral-7B Gen.
🔥🔥🔥[2024/08/02] We release the 7M foundational dataset Infinity-Instruct-7M.
🔥🔥🔥[2024/07/09] We release the model weights of InfInstruct-Mistral-7B 0625, InfInstruct-Qwen2-7B 0625, InfInstruct-Llama3-8B 0625, InfInstruct-Llama3-70B 0625, and InfInstruct-Yi-1.5-9B 0625.
🔥🔥🔥[2024/07/09] We release the chat dataset Infinity-Instruct-0625, it is a upgraded version of the Infinity-Instruct-0613.
🔥🔥🔥[2024/06/28] We release the model weight of InfInstruct-Llama3-70B 0613. It shows favorable results on AlpacaEval 2.0 compared to GPT4-0613 without RLHF.
🔥🔥🔥[2024/06/21] We release the model weight of InfInstruct-Mistral-7B 0613. It shows favorable results on AlpacaEval 2.0 compared to Mixtral 8x7B v0.1, Gemini Pro, and GPT-3.5 without RLHF.
🔥🔥🔥[2024/06/13] We share the intermediate result of our data construction process (corresponding to the InfInstruct-3M in the table below). Our ongoing efforts focus on risk assessment and data generation. The finalized version with 10 million instructions is scheduled for release in late June.

Training Details

Infinity-Instruct-7M-Gen-Llama3.1-70B is tuned on Million-level instruction dataset Infinity-Instruct. First, we apply the foundational dataset Infinity-Instruct-7M to improve the foundational ability (math & code) of Llama3.1-70B, and get the foundational instruct model Infinity-Instruct-7M-Llama3-70B. Then we finetune the Infinity-Instruct-7M-Llama3-70B to get the stronger chat model Infinity-Instruct-7M-Gen-Llama3_1-70B. Here is the training hyperparamers.

bash

epoch: 3
lr: 5e-6
min_lr: 0
lr_warmup_steps: 40
lr_decay_style: cosine
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
global_batch_size: 528
clip_grad: 1.0

Thanks to FlagScale, we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future!

Benchmark

Model	MT-Bench	AlpacaEval2.0	Arena-hard
GPT-4-0314	9.0	35.3	50.0
GPT-4-0613	9.2	30.2	37.9
GPT-4-1106	9.3	30.2	--
Llama-3-70B-Instruct	9.0	34.4	46.6
Llama-3.1-8B-Instruct	--	20.9	20.6
Llama-3.1-70B-Instruct	--	38.1	55.7
Llama-3.1-405B-Instruct	--	39.3	64.1
InfInstruct-7M-Llama-3.1-8B	8.2	33.9	30.4
InfInstruct-3M-0613-Llama-3-70B	8.7	31.5	--
InfInstruct-3M-0625-Llama-3-70B	8.9	38.0	--
InfInstruct-3M-Gen-Llama-3.1-70B	8.9	46.1	66.0

*denote the model is finetuned without reinforcement learning from human feedback (RLHF).

How to use

Infinity-Instruct-7M-Gen-Llama3_1-70B adopt the same chat template of Llama3-70B-instruct:

bash

<|begin_of_text|><|start_header_id|>user<|end_header_id|>

How are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Hi!<|eot_id|><|start_header_id|>user<|end_header_id|>

How are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

To apply this model and template in conversation scenarios, you can refer to the following code:

python

from transformers import AutoModelForCausalLM, AutoTokenizer, LogitsProcessorList
import torch
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("BAAI/Infinity-Instruct-7M-Gen-Llama3_1-70B")

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

logits_processor = LogitsProcessorList(
            [
                MinLengthLogitsProcessor(1, eos_token_id=tokenizer.eos_token_id),
                TemperatureLogitsWarper(0.7),
            ]
 )
 
generated_ids = model.generate(
    model_inputs.input_ids,
    logits_processor=logits_processor,
    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]
print(response)

Disclaimer

The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of Infinity Instruct is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.

Citation

Our paper, detailing the development and features of the Infinity Instruct dataset and finetuned models, will be released soon on arXiv. Stay tuned!

@article{InfinityInstruct2024,
  title={Infinity Instruct},
  author={Beijing Academy of Artificial Intelligence (BAAI)},
  journal={arXiv preprint arXiv:2406.XXXX},
  year={2024}
}