Update base_model formatting

19cb919 over 1 year ago

24.3 kB

	---
	language:
	- en
	license: llama2
	tags:
	- upstage
	- llama-2
	- instruct
	- instruction
	model_name: Llama 2 70B Instruct v2
	base_model: upstage/Llama-2-70b-instruct-v2
	inference: false
	model_creator: Upstage
	model_type: llama
	pipeline_tag: text-generation
	prompt_template: '### System:

	{system_message}


	### User:

	{prompt}


	### Assistant:

	'
	quantized_by: TheBloke
	---

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	# Llama 2 70B Instruct v2 - GGUF
	- Model creator: [Upstage](https://huggingface.co/Upstage)
	- Original model: [Llama 2 70B Instruct v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)

	<!-- description start -->
	## Description

	This repo contains GGUF format model files for [Upstage's Llama 2 70B Instruct v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2).

	<!-- description end -->
	<!-- README_GGUF.md-about-gguf start -->
	### About GGUF

	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

	Here is an incomplate list of clients and libraries that are known to support GGUF:

	* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
	* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
	* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
	* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.

	<!-- README_GGUF.md-about-gguf end -->
	<!-- repositories-available start -->
	## Repositories available

	* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-AWQ)
	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF)
	* [Upstage's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)
	<!-- repositories-available end -->

	<!-- prompt-template start -->
	## Prompt template: Orca-Hashes

	```
	### System:
	{system_message}

	### User:
	{prompt}

	### Assistant:

	```

	<!-- prompt-template end -->


	<!-- compatibility_gguf start -->
	## Compatibility

	These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)

	They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

	## Explanation of quantisation methods
	<details>
	<summary>Click to see details</summary>

	The new methods available are:
	* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
	* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
	* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
	* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
	* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

	Refer to the Provided Files table below to see what files use which methods, and how.
	</details>
	<!-- compatibility_gguf end -->

	<!-- README_GGUF.md-provided-files start -->
	## Provided files

	\| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [upstage-llama-2-70b-instruct-v2.Q2_K.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q2_K.gguf) \| Q2_K \| 2 \| 29.28 GB\| 31.78 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [upstage-llama-2-70b-instruct-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 29.92 GB\| 32.42 GB \| very small, high quality loss \|
	\| [upstage-llama-2-70b-instruct-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 33.19 GB\| 35.69 GB \| very small, high quality loss \|
	\| [upstage-llama-2-70b-instruct-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 36.15 GB\| 38.65 GB \| small, substantial quality loss \|
	\| [upstage-llama-2-70b-instruct-v2.Q4_0.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q4_0.gguf) \| Q4_0 \| 4 \| 38.87 GB\| 41.37 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [upstage-llama-2-70b-instruct-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 39.07 GB\| 41.57 GB \| small, greater quality loss \|
	\| [upstage-llama-2-70b-instruct-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 41.42 GB\| 43.92 GB \| medium, balanced quality - recommended \|
	\| [upstage-llama-2-70b-instruct-v2.Q5_0.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q5_0.gguf) \| Q5_0 \| 5 \| 47.46 GB\| 49.96 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [upstage-llama-2-70b-instruct-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 47.46 GB\| 49.96 GB \| large, low quality loss - recommended \|
	\| [upstage-llama-2-70b-instruct-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF/blob/main/upstage-llama-2-70b-instruct-v2.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 48.75 GB\| 51.25 GB \| large, very low quality loss - recommended \|
	\| upstage-llama-2-70b-instruct-v2.Q6_K.gguf \| Q6_K \| 6 \| 56.59 GB\| 59.09 GB \| very large, extremely low quality loss \|
	\| upstage-llama-2-70b-instruct-v2.Q8_0.gguf \| Q8_0 \| 8 \| 73.29 GB\| 75.79 GB \| very large, extremely low quality loss - not recommended \|

	Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

	### Q6_K and Q8_0 files are split and require joining

	Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

	<details>
	<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>

	### q6_K
	Please download:
	* `upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-a`
	* `upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-b`

	### q8_0
	Please download:
	* `upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-a`
	* `upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-b`

	To join the files, do the following:

	Linux and macOS:
	```
	cat upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-* > upstage-llama-2-70b-instruct-v2.Q6_K.gguf && rm upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-*
	cat upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-* > upstage-llama-2-70b-instruct-v2.Q8_0.gguf && rm upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-*
	```
	Windows command line:
	```
	COPY /B upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-a + upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-b upstage-llama-2-70b-instruct-v2.Q6_K.gguf
	del upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-a upstage-llama-2-70b-instruct-v2.Q6_K.gguf-split-b

	COPY /B upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-a + upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-b upstage-llama-2-70b-instruct-v2.Q8_0.gguf
	del upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-a upstage-llama-2-70b-instruct-v2.Q8_0.gguf-split-b
	```

	</details>
	<!-- README_GGUF.md-provided-files end -->

	<!-- README_GGUF.md-how-to-download start -->
	## How to download GGUF files

	Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

	The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
	- LM Studio
	- LoLLMS Web UI
	- Faraday.dev

	### In `text-generation-webui`

	Under Download Model, you can enter the model repo: TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF and below it, a specific filename to download, such as: upstage-llama-2-70b-instruct-v2.q4_K_M.gguf.

	Then click Download.

	### On the command line, including multiple files at once

	I recommend using the `huggingface-hub` Python library:

	```shell
	pip3 install huggingface-hub>=0.17.1
	```

	Then you can download any individual model file to the current directory, at high speed, with a command like this:

	```shell
	huggingface-cli download TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF upstage-llama-2-70b-instruct-v2.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
	```

	<details>
	<summary>More advanced huggingface-cli download usage</summary>

	You can also download multiple files at once with a pattern:

	```shell
	huggingface-cli download TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf'
	```

	For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

	To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

	```shell
	pip3 install hf_transfer
	```

	And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

	```shell
	HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF upstage-llama-2-70b-instruct-v2.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
	```

	Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
	</details>
	<!-- README_GGUF.md-how-to-download end -->

	<!-- README_GGUF.md-how-to-run start -->
	## Example `llama.cpp` command

	Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

	```shell
	./main -ngl 32 -m upstage-llama-2-70b-instruct-v2.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n\n### User:\n{prompt}\n\n### Assistant:"
	```

	Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

	Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

	If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

	For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

	## How to run in `text-generation-webui`

	Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).

	## How to run from Python code

	You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.

	### How to load this model from Python using ctransformers

	#### First install the package

	```bash
	# Base ctransformers with no GPU acceleration
	pip install ctransformers>=0.2.24
	# Or with CUDA GPU acceleration
	pip install ctransformers[cuda]>=0.2.24
	# Or with ROCm GPU acceleration
	CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
	# Or with Metal GPU acceleration for macOS systems
	CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
	```

	#### Simple example code to load one of these GGUF models

	```python
	from ctransformers import AutoModelForCausalLM

	# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
	llm = AutoModelForCausalLM.from_pretrained("TheBloke/Upstage-Llama-2-70B-instruct-v2-GGUF", model_file="upstage-llama-2-70b-instruct-v2.q4_K_M.gguf", model_type="llama", gpu_layers=50)

	print(llm("AI is going to"))
	```

	## How to use with LangChain

	Here's guides on using llama-cpp-python or ctransformers with LangChain:

	* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
	* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

	<!-- README_GGUF.md-how-to-run end -->

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	## Thanks, and how to contribute

	Thanks to the [chirper.ai](https://chirper.ai) team!

	Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

	I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

	If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

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	Thank you to all my generous patrons and donaters!

	And thank you again to a16z for their generous grant.

	<!-- footer end -->

	<!-- original-model-card start -->
	# Original model card: Upstage's Llama 2 70B Instruct v2

	# Updates
	Solar, a new bot created by Upstage, is now available on Poe. As a top-ranked model on the HuggingFace Open LLM leaderboard, and a fine tune of Llama 2, Solar is a great example of the progress enabled by open source.
	Try now at https://poe.com/Solar-0-70b


	# SOLAR-0-70b-16bit model card
	The model name has been changed from LLaMa-2-70b-instruct-v2 to SOLAR-0-70b-16bit

	## Model Details

	* Developed by: [Upstage](https://en.upstage.ai)
	* Backbone Model: [LLaMA-2](https://github.com/facebookresearch/llama/tree/main)
	* Language(s): English
	* Library: [HuggingFace Transformers](https://github.com/huggingface/transformers)
	* License: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
	* Where to send comments: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/Llama-2-70b-instruct-v2/discussions)
	* Contact: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai)

	## Dataset Details

	### Used Datasets
	- Orca-style dataset
	- Alpaca-style dataset
	- No other dataset was used except for the dataset mentioned above
	- No benchmark test set or the training set are used


	### Prompt Template
	```
	### System:
	{System}

	### User:
	{User}

	### Assistant:
	{Assistant}
	```

	## Usage

	- The followings are tested on A100 80GB
	- Our model can handle up to 10k+ input tokens, thanks to the `rope_scaling` option

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

	tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
	model = AutoModelForCausalLM.from_pretrained(
	"upstage/Llama-2-70b-instruct-v2",
	device_map="auto",
	torch_dtype=torch.float16,
	load_in_8bit=True,
	rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
	)

	prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	del inputs["token_type_ids"]
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

	output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
	output_text = tokenizer.decode(output[0], skip_special_tokens=True)
	```

	## Hardware and Software

	* Hardware: We utilized an A100x8 * 4 for training our model
	* Training Factors: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)

	## Evaluation Results

	### Overview
	- We conducted a performance evaluation following the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
	We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`
	We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463).
	- We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models

	### Main Results
	\| Model \| H4(Avg) \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| \| MT_Bench \|
	\|--------------------------------------------------------------------\|----------\|----------\|----------\|------\|----------\|-\|-------------\|
	\| [Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)(*Ours, Open LLM Leaderboard) \| 73* \| 71.1 \| 87.9 \| 70.6 \| 62.2 \| \| 7.44063 \|
	\| [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) \| 72.3 \| 70.9 \| 87.5 \| 69.8 \| 61 \| \| 7.24375 \|
	\| [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) \| 69.4 \| 67.6 \| 86.5 \| 64.9 \| 58.8 \| \| \|
	\| Llama-2-70b-hf \| 67.3 \| 67.3 \| 87.3 \| 69.8 \| 44.9 \| \| \|
	\| [llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048) (Ours, Open LLM Leaderboard) \| 67.0 \| 64.9 \| 84.9 \| 61.9 \| 56.3 \| \| \|
	\| [llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) (Ours, Open LLM Leaderboard) \| 65.2 \| 62.5 \| 86.2 \| 59.4 \| 52.8 \| \| \|
	\| llama-65b \| 64.2 \| 63.5 \| 86.1 \| 63.9 \| 43.4 \| \| \|
	\| falcon-40b-instruct \| 63.4 \| 61.6 \| 84.3 \| 55.4 \| 52.5 \| \| \|

	### Scripts for H4 Score Reproduction
	- Prepare evaluation environments:
	```
	# clone the repository
	git clone https://github.com/EleutherAI/lm-evaluation-harness.git
	# check out the specific commit
	git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
	# change to the repository directory
	cd lm-evaluation-harness
	```

	## Contact Us

	### About Upstage
	- [Upstage](https://en.upstage.ai) is a company specialized in Large Language Models (LLMs) and AI. We will help you build private LLMs and related applications.
	If you have a dataset to build domain specific LLMs or make LLM applications, please contact us at ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm)
	- As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally.

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