--- license: other license_name: nvidia-open-model-license license_link: >- https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf --- ## Nemotron-4-340B-Instruct [![Model architectuve](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)[![Model size](https://img.shields.io/badge/Params-340B-green)](#model-architecture)[![Language](https://img.shields.io/badge/Language-Multilingual-green)](#datasets) ### Model Overview Nemotron-4-340B-Instruct is a large language model (LLM) that can be used as part of a synthetic data generation pipeline to create training data that helps researchers and developers build their own LLMs; and is a fine-tuned version of the Nemotron-4-340B-Base model, optimized for English single and multi-turn chat use-cases. The base model was pre-trained on a corpus of 9 trillion tokens consisting of a diverse assortment of English based texts,50+ natural languages, and 40+ coding languages. Subsequently the Nemotron-4-340B-Instruct model went through additional alignment steps including: - Supervised Fine-tuning (SFT) - Direct Preference Optimization (DPO) - Additional in-house alignment technique: Reward-aware Preference Optimization (RPO) Throughout the alignment process, we relied on only approximately 20K human-annotated data while our data generation pipeline synthesized over 98% of the data used for supervised fine-tuning and preference fine-tuning (DPO & RPO). We provide comprehensive details about our synthetic data generation pipeline in the technical report. This results in a model that is aligned for human chat preferences, improvements in mathematical reasoning, coding and instruction-following, and is capable of generating high quality synthetic data for a variety of use cases. Under the NVIDIA Open Model License, NVIDIA confirms: - Models are commercially usable. - You are free to create and distribute Derivative Models. - NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models. ### License: [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf) ### Intended use Nemotron-4-340B-Instruct is a chat model intended for use for the English language. Nemotron-4-340B-Instruct is designed for Synthetic Data Generation to enable developers and enterprises for building and customizing their own large language models and LLM applications. The instruct model itself can be further customized using the [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html) suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/Steer-LM/RLHF using [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner). **Model Developer:** NVIDIA **Model Input:** Text **Input Format:** String **Input Parameters:** One-Dimensional (1D) **Model Output:** Text **Output Format:** String **Output Parameters:** 1D **Model Dates:** Nemotron-4-340B-Instruct was trained between December 2023 and May 2024 **Data Freshness:** The pretraining data has a cutoff of June 2023 ### Required Hardware BF16 Inference: - 8x H200 (1x H200 Node) - 16x H100 (2x H100 Nodes) - 16x A100 (2x A100 Nodes) FP8 Inference: - 8x H100 (1x H100 Node) ### Model Architecture: Nemotron-4-340B-Base, is standard decoder-only Transformer, trained with a sequence length of 4096 tokens, uses Grouped-Query Attention (GQA), and Rotary Position Embeddings (RoPE). **Architecture Type:** Transformer Decoder (auto-regressive language model) **Network Architecture:** Nemotron-4 ### Usage 1. We will spin up an inference server and then call the inference server in a python script. Let’s first define the python script ``call_server.py`` ```python import json import requests headers = {"Content-Type": "application/json"} def text_generation(data, ip='localhost', port=None): resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers) return resp.json() def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False): data = { "sentences": [prompt] if not batch else prompt, "tokens_to_generate": int(token_to_gen), "temperature": temp, "add_BOS": add_BOS, "top_k": top_k, "top_p": top_p, "greedy": greedy, "all_probs": False, "repetition_penalty": repetition, "min_tokens_to_generate": int(min_tokens), "end_strings": ["<|endoftext|>", "", "\x11", "User"], } sentences = text_generation(data, port=1424)['sentences'] return sentences[0] if not batch else sentences PROMPT_TEMPLATE = """System User {prompt} Assistant """ question = "Write a poem on NVIDIA in the style of Shakespeare" prompt = PROMPT_TEMPLATE.format(prompt=question) print(prompt) response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False) response = response[len(prompt):] if response.endswith(""): response = response[:-len("")] print(response) ``` 2. Given this python script, we will create a bash script, which spins up the inference server within the NeMo container (```docker pull nvcr.io/nvidia/nemo:24.01.framework```) and calls the python script ``call_server.py``. The bash script ``nemo_inference.sh`` is as follows, ```bash NEMO_FILE=$1 WEB_PORT=1424 depends_on () { HOST=$1 PORT=$2 STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?) while [ $STATUS -ne 0 ] do echo "waiting for server ($HOST:$PORT) to be up" sleep 10 STATUS=$(curl -X PUT http://$HOST:$PORT >/dev/null 2>/dev/null; echo $?) done echo "server ($HOST:$PORT) is up running" } /usr/bin/python3 /opt/NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py \ gpt_model_file=$NEMO_FILE \ pipeline_model_parallel_split_rank=0 \ server=True tensor_model_parallel_size=8 \ trainer.precision=bf16 pipeline_model_parallel_size=2 \ trainer.devices=8 \ trainer.num_nodes=2 \ web_server=False \ port=${WEB_PORT} & SERVER_PID=$! readonly local_rank="${LOCAL_RANK:=${SLURM_LOCALID:=${OMPI_COMM_WORLD_LOCAL_RANK:-}}}" if [ $SLURM_NODEID -eq 0 ] && [ $local_rank -eq 0 ]; then depends_on "0.0.0.0" ${WEB_PORT} echo "start get json" sleep 5 echo "SLURM_NODEID: $SLURM_NODEID" echo "local_rank: $local_rank" /usr/bin/python3 /scripts/call_server.py echo "clean up dameons: $$" kill -9 $SERVER_PID pkill python fi wait ``` 3, We can launch the ``nemo_inferece.sh`` with a slurm script defined like below, which starts a 2-node job for the model inference. ```bash #!/bin/bash #SBATCH -A SLURM-ACCOUNT #SBATCH -p SLURM-PARITION #SBATCH -N 2 # number of nodes #SBATCH -J generation #SBATCH --ntasks-per-node=8 #SBATCH --gpus-per-node=8 set -x RESULTS= OUTFILE="${RESULTS}/slurm-%j-%n.out" ERRFILE="${RESULTS}/error-%j-%n.out" MODEL=/Nemotron-4-340B-Instruct MOUNTS="--container-mounts=:/scripts,MODEL:/model" read -r -d '' cmd <