Llama 13b requirements. Deploy. To stop LlamaGPT, do Ctrl + C in Terminal. Look at "Version" to see what version you are running. I even finetuned my own models to the GGML format and a 13B uses only 8GB of RAM (no GPU, just CPU) using llama. According to the FAIR team, LLaMA-13B, which is one of the models in the collection, performed better than GPT-3 (175B) in most tests or evaluations So far the demo of the 7b alpaca model is more impressive than what I've been able to get out of the 13b llama model. Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These powerful models hold great potential for a wide range of applications. This release includes model weights and starting code for pre-trained and fine-tuned Llama language models — ranging from 7B to 70B parameters. But for the GGML / GGUF format, it's more about having enough RAM. So if by 100% it were using 14GB per 10%, total RAM usage would be 220GB for 7B 64k. Meta Code Llama. cpp and libraries and UIs which support this format, such as: KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. I’ll be using a collab notebook but you can use your local machine, it just needs to have around 12 Gb of VRAM. 8 tokens/sec). Model version This is version 1 of the model. All models are trained with a batch size of 4M tokens. Code Llama is state-of-the-art for publicly available LLMs on coding Jan 2, 2024 · In contrast, LLaMA 2 13B, despite slower inference speed, demands higher resources, limiting its accessibility due to these elevated hardware requirements. Today, we’re releasing Code Llama, a large language model (LLM) that can use text prompts to generate and discuss code. Continue. Llama 2 is a large language AI model capable of generating text and code in response to prompts. Unlock your creativity with 1+ free Code-Llama-13b Project-requirements Prompts on PromptPal. Train. Our latest version of Llama – Llama 2 – is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly. The code of the implementation in Hugging Face is based on GPT-NeoX TL;DR: we are releasing our public preview of OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA. 36 MB (+ 1280. AI models generate responses and outputs based on complex algorithms and machine learning techniques, and those responses or outputs may be inaccurate or indecent. Jul 21, 2023 · @HamidShojanazeri is it possible to use the Llama2 base model architecture and train the model with any one non-english language?. Although the LLaMa models were trained on A100 80GB GPUs it is possible to run the models on different and smaller multi-GPU hardware for inference. Plain C/C++ implementation without any dependencies. Meta Llama 3. The hardware requirements will vary based on the model size deployed to SageMaker. This model was contributed by zphang with contributions from BlackSamorez. Llama 3 is an accessible, open-source large language model (LLM) designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas. In this repo, we present a permissively licensed open source reproduction of Meta AI's LLaMA large language model. In the Model dropdown, choose the model you just downloaded: llama-2-13B-Guanaco-QLoRA-GPTQ. Feb 24, 2023 · Unlike the data center requirements for GPT-3 derivatives, LLaMA-13B opens the door for ChatGPT-like performance on consumer-level hardware in the near future. I also benchmark ExLlamaV2’s computational cost for quantization. cpp's chat-with-vicuna-v1. Resources. The model will automatically load, and is now ready for use! If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. com. Vicuna-13B is built by fine-tuning the LLaMA architecture on a dataset of approximately 70,000 multi-turn conversations collected from ShareGPT. In this part, we will learn about all the steps required to fine-tune the Llama 2 model with 7 billion parameters on a T4 GPU. Note: On the first run, it may take a while for the model to be downloaded to the /models directory. 65B/70B requires a 48GB card, or 2 x 24GB. The model could fit into 2 consumer GPUs. vLLM: An open source, high-throughput, and memory-efficient inference and serving engine for LLMs from UC Berkeley. This model is designed for general code synthesis and understanding. While testing both models, we felt that Mistral 7B model is taking less time (average time 13 to 20 seconds) to respond than the LLaMA 2 13B (average time 33 to 35 seconds) Feb 24, 2023 · Abstract. Meta Llama Guard 2 Recommended. Nov 14, 2023 · If the 7B CodeLlama-13B-GPTQ model is what you're after, you gotta think about hardware in two ways. A: it is a pair format with three columns: text1, text2, and label (0/1). Llama 2 13B. Code Llama is an AI model built on top of Llama 2, fine-tuned for generating and discussing code. The chat model is fine-tuned using Aug 7, 2023 · 3. Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. 2023. With GPTQ quantization, we can further reduce the precision to 3-bit without losing much in the performance of the model. PLaMo-13B Release blog (Japanese) Usage Requirements numpy; sentencepiece; torch; transformers; Use a pipeline as a high-level helper llama_model_load_internal: ggml ctx size = 0. As of right now GPTQ-for-LLaMA is using a VRAM hungry attention method. System Requirements. For beefier models like the open-llama-13b-open-instruct-GGML, you'll need more powerful hardware. The chat model is fine-tuned using Jul 24, 2023 · Models in the catalog are organized by collections. py --gptq-bits 4 --model llama-7b Oct 29, 2023 · Costs about ~$120USD, and with 64GB RAM you can run up to 70B models (I get about 0. yaml for llama2-13b-chat is as follows: 1 engine: 2 model: /Llama-2-13b-chat-hf/ 3 tensor_parallel_size: 2 4 dtype: float16. At least 32 GB of RAM for the 70B models. Prompt 1 Large language model. This command will enable WSL, download and install the lastest Linux Kernel, use WSL2 as default, and download and install the Ubuntu Linux distribution. Open Transferring this to Llama recipe repo where we are a lot of fine tuning examples. Aug 31, 2023 · For 13B Parameter Models. 4 for GPT I agree with both of you - in my recent evaluation of the best models, gpt4-x-vicuna-13B and Wizard-Vicuna-13B-Uncensored tied with GPT4-X-Alpasta-30b (which is a 30B model!) and easily beat all the other 13B and 7B models including WizardLM (censored and uncensored variants), Vicuna (censored and uncensored variants), GPT4All-13B-snoozy, StableVicuna, Llama-13B-SuperCOT, Koala, and Alpaca. Model type LLaMA is an auto-regressive language model, based on the transformer architecture. PP shards layers. Trust & Safety. 2022 and Feb. Description. 0T tokens. cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud. We release all our models to the research community. Hardware requirements. Batch size and gradient accumulation steps affect learning rate that you should use, 0. We introduce LLaMA, a collection of founda- tion language models ranging from 7B to 65B parameters. The smaller models were trained on 1. 16 GB to run the 13B models, and 32 GB to run the 33B models. Model details. The code runs on both platforms. People always confuse them. 2-GGML, you'll need more powerful hardware. These models vary in size, with the smallest having 7 billion parameters and the largest having 70 billion parameters. If you're using the GPTQ version, you'll want a strong GPU with at least 10 gigs of VRAM. Sc0urge. Note: We haven't tested GPTQ models yet. Apr 26, 2024 · An example of model_config. 5G RAM per 10% of the prompt at 20% through, then 5. This repo contains GPTQ model files for KoboldAI's Llama2 13B Tiefighter. Part of a foundational system, it serves as a bedrock for innovation in the global community. Alternatively, hit Windows+R, type msinfo32 into the "Open" field, and then hit enter. 3GB per 10% at 30%, and 7GB per 10% at 50% of the prompt. We are releasing a series of 3B, 7B and 13B models trained on different data mixtures. This model is fine-tuned based on Meta Platform’s Llama 2 Chat open source model. Just download the repo using git clone, and follow the instructions for setup. If you use AdaFactor, then you need 4 bytes per parameter, or 28 GB of GPU memory. PLaMo-13B is released under Apache v2. How to Fine-Tune Llama 2: A Step-By-Step Guide. Meta says that "it’s likely that you can fine-tune the Llama 2-13B model using LoRA or QLoRA fine-tuning with a single consumer GPU with 24GB of memory, and using QLoRA requires even less GPU memory and fine-tuning time than LoRA" in their fine-tuning guide. Model date LLaMA was trained between December. Parameter size is a big deal in AI. I probably don't have those figures right, but Mar 21, 2023 · In case you use regular AdamW, then you need 8 bytes per parameter (as it not only stores the parameters, but also their gradients and second order gradients). Aug 3, 2023 · The GPU requirements depend on how GPTQ inference is done. Aug 9, 2023 · Minimum requirements for llama2-13b and llama2-70b fine-tuning #170. I've tested it on an RTX 4090, and it reportedly works on the 3090. Download the model. 00 MB per state) llama_model_load_internal: allocating batch_size x (1536 kB + n_ctx x 416 B) = 1600 MB VRAM for the scratch buffer 欢迎来到Llama中文社区！我们是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。 已经基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级【Done】。 Llama 2. LLaMA Model Card Model details Organization developing the model The FAIR team of Meta AI. Llama 2. If you use ExLlama, which is the most performant and efficient GPTQ library at the moment, then: 7B requires a 6GB card. The GPTQ-for-LLaMA repo supports 3-bit quantization and inference. Getting Started. A preliminary evaluation using GPT-4 as a judge showed Vicuna-13B achieving more than 90% quality of chatGPT and Google Bard, then outperformed other models like LLaMa and Alpaca in more than 90% Aug 31, 2023 · For beefier models like the WizardLM-13B-V1. Get up and running with Llama 3, Mistral, Gemma, and other large language models. The GTX 1660 or 2060, AMD 5700 XT, or RTX 3050 or 3060 would all work nicely. You have the option to use a free GPU on Google Colab or Kaggle. Use in Transformers. First, for the GPTQ version, you'll want a decent GPU with at least 6GB VRAM. You should try it, coherence and general results are so much better with 13b models. Hence, for a 7B model you would need 8 bytes per parameter * 7 billion parameters = 56 GB of GPU memory. The paper shows that training smaller foundation models on large enough tokens is desirable, as it requires less computing power and resources. So this extra 25% savings is already possible. Llama 2 chat chinese fine-tuned model. LLaMA 7B LLaMA 13B LLaMA 33B LLaMA 65B Figure 1: Training loss over train tokens for the 7B, 13B, 33B, and 65 models. B: it is a triple format with three columns: text, positive, and negative. Llama 2: open source, free for research and commercial use. 3. 0001 should be fine with batch size 1 and gradient accumulation steps 1 on llama 2 13B, but for bigger models you tend to decrease lr, and for higher batch size you tend to increase lr. 13B MP is 2 and required 27GB VRAM. Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks. positive and negative store the positive and negative samples of text. This model is fine-tuned based on Meta Platform’s Llama 2 Chat open source Mar 2, 2023 · True. Browse our large catalogue of Events prompts and get inspired and more productive today. We will see that the resulting models are very fast for inference. Output Models generate text only. This is the repository for the base 13B version in the Hugging Face Transformers format. Llama2 7B Llama2 7B-chat Llama2 13B Llama2 13B-chat Llama2 70B Llama2 70B-chat 由于 Llama 2 本身的中文对齐比较弱，开发者采用了中文指令集来进行微调，使其具备较强的中文对话能力。. The Colab T4 GPU has a limited 16 GB of VRAM. Quantization doesn't affect the context size memory requirements very much At 64k context you might be looking at somewhere in the neighborhood of ~100GB of memory See translation. The non-bolded is the input and the bolded is the output from the model. - ollama/ollama. I used all the default settings from the webgui. Applying the XORs The model weights in this repository cannot be used as-is. 30B/33B requires a 24GB card, or 2 x 12GB. While this article focuses on a specific model in the Llama 2 family, you can apply the same methodology to other The main goal of llama. LoLLMS Web UI, a great web UI with GPU acceleration via the Oct 31, 2023 · Each of these models comes in three sizes, with 7B, 13B, and 34B parameters, catering to different levels of complexity and computational requirements. According to Meta, Llama 2 is trained on 2 trillion tokens, and the context length is increased to 4096. Organization developing the model The FAIR team of Meta AI. A summary of the minimum GPU requirements and recommended AIME systems to run a specific LLaMa model with near realtime reading performance: Aug 16, 2023 · Llama 2 isn’t just one model; it’s a collection of models. Aug 24, 2023 · Code Llama is a state-of-the-art LLM capable of generating code, and natural language about code, from both code and natural language prompts. At least 16 GB of RAM for the 13B models. Model date Llama was trained between December. Especially good for story telling. The notebook demonstrating mixed-precision quantization of Llama 2 with ExLlamaV2 is available here: Get the notebook (#18) Share In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. Mar 7, 2023 · There are four different pre-trained LLaMA models, with 7B (billion), 13B, 30B, and 65B parameters. We're unlocking the power of these large language models. Model variants Sep 1, 2023 · But getting some very rough figures: It used an additional 3. Model Details. The collection contains pretrained and fine-tuned variants of the 7B, 13B and 70B-parameter Llama 2 generative text models. Oct 10, 2023 · Requirements. CubeEONZ. A preliminary evaluation using GPT-4 as a judge showed Vicuna-13B achieving more than 90% quality of chatGPT and Google Bard, then outperformed other models like LLaMa and Alpaca in more than 90% . cpp. Though maybe it'd be even higher than that. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project. Here's what's generally recommended: At least 8 GB of RAM is suggested for the 7B models. Meta reports the 65B model is on-parr with Google's PaLM-540B in terms of performance. 9 concurrent sessions (24GB VRAM pushed to the max): 619 tokens/s. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. Links to other models can be found in the index at the bottom. You may also see lots of The Open Assistant model is a LLaMA 33B model finetuned with Reinforcement Learning from Human Feedback (RLHF) on the same OASST1 dataset that we experiment with. If the Code Llama models (7B/13B/34B) are not yielding satisfactory results for a specific task, such as converting text to SQL, fine-tuning the model may be necessary. Quick and early benchmark with llama2-chat-13b batch 1 AWQ int4 with int8 KV cache on RTX 4090: 1 concurrent session: 105 tokens/s. Flash attention will reduce the requirements for 7B to 4GB and possibly fit 30B with a 2048 context window into 16GB, all before stacking 3-bit. If you’re not familiar with the Huggingface ecosystem of Python packages, what we’re doing here is importing some of their convenience classes (the ones that start with “Auto”) to load up our model and tokenizer by name, then pushing the model into VRAM with model. Edit model card. The code of the implementation in Hugging Face is based on GPT-NeoX Aug 31, 2023 · For 13B Parameter Models. To get the expected features and performance for the 7B, 13B and 34B variants, a specific formatting defined in chat_completion() needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and linebreaks in between (we recommend calling strip() on inputs to avoid double-spaces). May 14, 2023 · If you have more VRAM, you can increase the number -ngl 18 to -ngl 24 or so, up to all 40 layers in llama 13B. PLaMo-13B Model Description PLaMo-13B is a LLaMA-based 13B model pre-trained on English and Japanese open datasets, developed by Preferred Networks, Inc. Stanford announces it is in contact with Meta regarding the release of the Alpaca model weights. 8 concurrent sessions: 580 tokens/s. This guide provides information and resources to help you set up Meta Llama including how to access the model, hosting, how-to and integration guides. 13B requires a 10GB card. Code Llama is free for research and commercial use. Code Llama is built on top of Llama 2 and is available in three models: Code Llama, the foundational code model; Codel Llama - Python specialized for Jul 18, 2023 · Memory requirements. 7 GB of VRAM usage and let the models use the rest of your system ram. The model comes in different sizes: 7B, 13B, 33B Instructions for converting weights can be found here. You can easily run 13b quantized models on your 3070 with amazing performance using llama. 5 bytes). Anyway, the requirements for 5TPS on 7B models are very modest. LLaMA comes in four size variants: 7B, 13B, 33B, and 65B parameters. Use the safetensors version of the model, the pt version is an old quantization that is no longer supported and will be removed in the future. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Model type Llama is an auto-regressive language model, based on the transformer architecture. Let's jump into system requirements. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. In the top left, click the refresh icon next to Model. Llama 2 comes in 3 different sizes - 7B, 13B & 70B parameters. Model creator: KoboldAI. Apr 5, 2023 · 8. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. 1. llama-13b-int4. This is version 1. The tuned versions use supervised fine Jul 14, 2023 · Recently, numerous open-source large language models (LLMs) have been launched. Input Models input text only. Since then I upgraded and now I run int8, and q4 models. All of this along with the training scripts for doing finetuning using Alpaca has been pulled together in the github repository, Alpaca-Lora. Aug 8, 2023 · We'll be using the TheBloke/Llama-2-13B-chat-GGML model for this guide. Apr 6, 2023 · What is LLaMA 🦙 LLaMA is a foundational large language model that has been released by Meta AI. Mar 20, 2023 · For the Alpaca-LoRA implementation there already exists a fine-tuned version of the LLaMA-13B model. Jul 24, 2023 · Fine Tune Llama-2-13b on a single GPU on custom data. So it can run in a single A100 80GB or 40GB, but after modying the model. If we quantize Llama 2 70B to 4-bit precision, we still need 35 GB of memory (70 billion * 0. 119K subscribers in the LocalLLaMA community. Select the safety guards you want to add to your modelLearn more about Llama Guard and best practices for developers in our Responsible Use Guide. Having even a fairly weak GPU is helpful even if you can't offload much, since it really speeds up processing long prompts. It relies almost entirely on the bitsandbytes and LLM. It's slow but not unusable (about 3-4 tokens/sec on a Ryzen 5900) Code Llama - Instruct models are fine-tuned to follow instructions. GGML files are for CPU + GPU inference using llama. It's probably not as good, but good luck finding someone with full fine Vicuna-13B is an open-source conversational model trained from fine-tuning the LLaMa 13B model using user-shared conversations gathered from ShareGPT. Jul 25, 2023 · Training Vicuna-13B with Real ChatGPT Conversations. 9% on MMLU. Like from the scratch using Llama base model architecture but with my non-english language data? not with the data which Llama was trained on. 21 MB llama_model_load_internal: using CUDA for GPU acceleration llama_model_load_internal: mem required = 22944. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases. Data Prepation. Jul 21, 2023 · what are the minimum hardware requirements to run the models on a local machine ? Requirements CPU : GPU: Ram: For All models. For the CPU infgerence (GGML / GGUF) format, having enough RAM is key. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Below is a set up minimum requirements for each model size we tested. Links to other models can be found in the index at the bottom Aug 9, 2023 · We show how to extend it to provide mappings between the interface requirements of the model deployment resource. steps, and vary the learning rate and batch size with Sep 27, 2023 · For smaller GPUs, I show how to quantize Llama 2 13B with mixed precision. Aug 24, 2023 · Takeaways. Technology. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. The only comparison against GPT 3. 2. In this tutorial, we will walk through each step of fine-tuning Llama-2-13b model on a single GPU. Vicuna does full fine-tuning of LLaMA 13B on proprietary user-shared conversations from ShareGPT and is thus the result of distillation from OpenAI GPT models. It will run faster if you put more layers into the GPU Select the models you would like access to. For more detailed examples leveraging Hugging Face, see llama-recipes. It might also theoretically allow us to run LLaMA-65B Llama2 13B Tiefighter - GPTQ. While platforms like Google Colab Pro offer the ability to test up to 7B models, Continue reading How to run LLaMA-13B or Mar 19, 2023 · (Replace llama-7b with llama-13b if that's what you downloaded; many other models exist and may generate better, or at least different, results. Meta Llama 2. Aside: if you don't know, Model Parallel (MP) encompasses both Pipeline Parallel (PP) and Tensor Parallel (TP). This LoRA trained for 3 epochs and has been converted to int4 (4bit) via GPTQ method. ) python server. The first one I ran was the original Llama fp16. To run 13B or 70B chat models, replace 7b with 13b or 70b respectively. The 65B parameter Read more » Sep 28, 2023 · A high-end consumer GPU, such as the NVIDIA RTX 3090 or 4090, has 24 GB of VRAM. Links to other models can be found in Sep 3, 2023 · For the full 128k context with 13b model, it's ~360GB of VRAM (or RAM if using CPU inference) for fp16 inference. 4T tokens. Original model: Llama2 13B Tiefighter. Mar 26, 2023 · Finetuning Llama 13B on a 24G GPU. Model Details Pygmalion 13B is a dialogue model based on Meta's LLaMA-13B. This repository is intended as a minimal example to load Llama 2 models and run inference. Vicuna-13B is an open-source conversational model trained from fine-tuning the LLaMa 13B model using user-shared conversations gathered from ShareGPT. It’s free for research and commercial use. Offload 20-24 layers to your gpu for 6. TP shards each tensor. Meta reports that the LLaMA-13B model outperforms GPT-3 in most benchmarks. This example config file specifies a 2 GPU deployment – depending on your model and GPU, you may be able to modify the config file and deploy with more or fewer GPUs. We are releasing 3B, 7B and 13B models trained on 1T tokens. These conversations contain real examples of how users interact with ChatGPT, helping teach the model to converse naturally. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. Feb 24, 2023 · LLaMA-13B Outperforms GPT-3 on Most Benchmarks. DatasetFormats. There are also a couple of PRs waiting that should crank these up a bit. Pygmalion 13B A conversational LLaMA fine-tune. Reply. Meta Code LlamaLLM capable of generating code, and natural Code Llama. to("cuda"). 5 to 7. 目前这个中文微调参数模型总共发布了 7B，13B两种参数大小。. 5 I found in the LLaMA paper was not in favor of LLaMA: Despite the simplicity of the instruction finetuning approach used here, we reach 68. int8() work of Tim Dettmers. Community. See the repo below for more info. We support two dataset formats: DatasetFormats. May 15, 2023 · Simple enough. Note: This is a forked repository with some minor deltas from the upstream. 0 license. AMD 6900 XT, RTX 2060 12GB, RTX 3060 12GB, or RTX 3080 would do the trick. Our model weights can serve as the drop in replacement of LLaMA in existing implementations. LLaMA-I (65B) outperforms on MMLU existing instruction finetuned models of moderate sizes, but are still far from the state-of-the-art, that is 77. These files are GGML format model files for Meta's LLaMA 13b. However, one major challenge that arises is the limitation of resources when it comes to testing these models. txt 目前这个中文微调参数模型总共发布了 7B，13B两种参数大小。. Here are a few examples of the outputs, not cherry picked to make it look good or bad. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. LLaMA-33B and LLaMA-65B were trained on 1. The Vicuna 13B model needs ~10GB of CPU RAM, If you don't have enough RAM, Example of how to run the 13b model with llama. 7b models generally require at least 8GB of RAM; 13b models generally require at least 16GB of RAM; 70b models generally require at least 64GB of RAM; If you run into issues with higher quantization levels, try using the q4 model or shut down any other programs that are using a lot of memory. You can view models linked from the ‘Introducing Llama 2’ tile or filter on the ‘Meta’ collection, to get started with the Llama 2 models. To run Code Llama 7B, 13B or 34B models, replace 7b with code-7b, code-13b or code-34b respectively.
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