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--- |
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language: |
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- en |
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tags: |
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- causal-lm |
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- llama |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- OpenAssistant/oasst1 |
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- nomic-ai/gpt4all_prompt_generations |
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- tatsu-lab/alpaca |
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inference: false |
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--- |
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<div style="width: 100%;"> |
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p><a href="https://discord.gg/UBgz4VXf">Chat & support: my new Discord server</a></p> |
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</div> |
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<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? Patreon coming soon!</a></p> |
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</div> |
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</div> |
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# StableVicuna-13B-GPTQ |
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This repo contains 4bit GPTQ format quantised models of [CarperAI's StableVicuna 13B](https://huggingface.co/CarperAI/stable-vicuna-13b-delta). |
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It is the result of first merging the deltas from the above repository with the original Llama 13B weights, then quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). |
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## Repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/stable-vicuna-13B-GPTQ). |
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* [4-bit, 5-bit and 8-bit GGML models for CPU (+CUDA) inference](https://huggingface.co/TheBloke/stable-vicuna-13B-GGML). |
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* [Unquantised float16 model in HF format](https://huggingface.co/TheBloke/stable-vicuna-13B-HF). |
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## PROMPT TEMPLATE |
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This model works best with the following prompt template: |
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``` |
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### Human: your prompt here |
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### Assistant: |
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``` |
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## How to easily download and use this model in text-generation-webui |
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Open the text-generation-webui UI as normal. |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/stable-vicuna-13B-GPTQ`. |
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3. Click **Download**. |
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4. Wait until it says it's finished downloading. |
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5. Click the **Refresh** icon next to **Model** in the top left. |
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6. In the **Model drop-down**: choose the model you just downloaded,`stable-vicuna-13B-GPTQ`. |
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7. If you see an error in the bottom right, ignore it - it's temporary. |
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8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama` |
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9. Click **Save settings for this model** in the top right. |
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10. Click **Reload the Model** in the top right. |
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11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! |
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## Provided files |
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I have uploaded two versions of the GPTQ. |
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**Compatible file - stable-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors** |
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In the `main` branch - the default one - you will find `stable-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors` |
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This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility |
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It was created without the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui. |
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* `stable-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors` |
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* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches |
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* Works with text-generation-webui one-click-installers |
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* Parameters: Groupsize = 128g. No act-order. |
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* Command used to create the GPTQ: |
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``` |
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CUDA_VISIBLE_DEVICES=0 python3 llama.py stable-vicuna-13B-HF c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors stable-vicuna-13B-GPTQ-4bit.no-act-order.safetensors |
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``` |
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**Latest file - stable-vicuna-13B-GPTQ-4bit.latest.act-order.safetensors** |
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Created for more recent versions of GPTQ-for-LLaMa, and uses the `--act-order` flag for maximum theoretical performance. |
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To access this file, please switch to the `latest` branch fo this repo and download from there. |
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* `stable-vicuna-13B-GPTQ-4bit.latest.act-order.safetensors` |
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* Only works with recent GPTQ-for-LLaMa code |
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* **Does not** work with text-generation-webui one-click-installers |
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* Parameters: Groupsize = 128g. **act-order**. |
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* Offers highest quality quantisation, but requires recent GPTQ-for-LLaMa code |
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* Command used to create the GPTQ: |
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``` |
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CUDA_VISIBLE_DEVICES=0 python3 llama.py stable-vicuna-13B-HF c4 --wbits 4 --true-sequential --act-order --groupsize 128 --save_safetensors stable-vicuna-13B-GPTQ-4bit.act-order.safetensors |
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``` |
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## Manual instructions for `text-generation-webui` |
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File `stable-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors` can be loaded the same as any other GPTQ file, without requiring any updates to [oobaboogas text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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[Instructions on using GPTQ 4bit files in text-generation-webui are here](https://github.com/oobabooga/text-generation-webui/wiki/GPTQ-models-\(4-bit-mode\)). |
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The other `safetensors` model file was created using `--act-order` to give the maximum possible quantisation quality, but this means it requires that the latest GPTQ-for-LLaMa is used inside the UI. |
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If you want to use the act-order `safetensors` files and need to update the Triton branch of GPTQ-for-LLaMa, here are the commands I used to clone the Triton branch of GPTQ-for-LLaMa, clone text-generation-webui, and install GPTQ into the UI: |
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``` |
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# Clone text-generation-webui, if you don't already have it |
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git clone https://github.com/oobabooga/text-generation-webui |
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# Make a repositories directory |
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mkdir text-generation-webui/repositories |
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cd text-generation-webui/repositories |
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# Clone the latest GPTQ-for-LLaMa code inside text-generation-webui |
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git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa |
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``` |
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Then install this model into `text-generation-webui/models` and launch the UI as follows: |
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``` |
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cd text-generation-webui |
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python server.py --model stable-vicuna-13B-GPTQ --wbits 4 --groupsize 128 --model_type Llama # add any other command line args you want |
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``` |
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The above commands assume you have installed all dependencies for GPTQ-for-LLaMa and text-generation-webui. Please see their respective repositories for further information. |
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If you can't update GPTQ-for-LLaMa or don't want to, you can use `stable-vicuna-13B-GPTQ-4bit.no-act-order.safetensors` as mentioned above, which should work without any upgrades to text-generation-webui. |
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## Want to support my work? |
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I've had a lot of people ask if they can contribute. I love providing models and helping people, but it is starting to rack up pretty big cloud computing bills. |
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So if you're able and willing to contribute, it'd be most gratefully received and will help me to keep providing models, and work on various AI projects. |
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Donaters will get priority support on any and all AI/LLM/model questions, and I'll gladly quantise any model you'd like to try. |
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* Patreon: coming soon! (just awaiting approval) |
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* Ko-Fi: https://ko-fi.com/TheBlokeAI |
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* Discord: https://discord.gg/UBgz4VXf |
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# Original StableVicuna-13B model card |
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## Model Description |
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StableVicuna-13B is a [Vicuna-13B v0](https://huggingface.co/lmsys/vicuna-13b-delta-v0) model fine-tuned using reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO) on various conversational and instructional datasets. |
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## Model Details |
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* **Trained by**: [Duy Phung](https://github.com/PhungVanDuy) of [CarperAI](https://carper.ai) |
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* **Model type:** **StableVicuna-13B** is an auto-regressive language model based on the LLaMA transformer architecture. |
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* **Language(s)**: English |
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* **Library**: [trlX](https://github.com/CarperAI/trlx) |
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* **License for delta weights**: [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
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* *Note*: License for the base LLaMA model's weights is Meta's [non-commercial bespoke license](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md). |
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* **Contact**: For questions and comments about the model, visit the [CarperAI](https://discord.com/invite/KgfkCVYHdu) and [StableFoundation](https://discord.gg/stablediffusion) Discord servers. |
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| Hyperparameter | Value | |
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|---------------------------|-------| |
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| \\(n_\text{parameters}\\) | 13B | |
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| \\(d_\text{model}\\) | 5120 | |
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| \\(n_\text{layers}\\) | 40 | |
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| \\(n_\text{heads}\\) | 40 | |
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## Training |
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### Training Dataset |
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StableVicuna-13B is fine-tuned on a mix of three datasets. [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages distributed across 66,497 conversation trees, in 35 different languages; |
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[GPT4All Prompt Generations](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations), a dataset of 400k prompts and responses generated by GPT-4; and [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. |
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The reward model used during RLHF was also trained on [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1) along with two other datasets: [Anthropic HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), a dataset of preferences about AI assistant helpfulness and harmlessness; and [Stanford Human Preferences Dataset](https://huggingface.co/datasets/stanfordnlp/SHP) a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice. |
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### Training Procedure |
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`CarperAI/stable-vicuna-13b-delta` was trained using PPO as implemented in [`trlX`](https://github.com/CarperAI/trlx/blob/main/trlx/trainer/accelerate_ppo_trainer.py) with the following configuration: |
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| Hyperparameter | Value | |
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|-------------------|---------| |
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| num_rollouts | 128 | |
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| chunk_size | 16 | |
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| ppo_epochs | 4 | |
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| init_kl_coef | 0.1 | |
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| target | 6 | |
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| horizon | 10000 | |
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| gamma | 1 | |
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| lam | 0.95 | |
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| cliprange | 0.2 | |
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| cliprange_value | 0.2 | |
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| vf_coef | 1.0 | |
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| scale_reward | None | |
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| cliprange_reward | 10 | |
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| generation_kwargs | | |
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| max_length | 512 | |
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| min_length | 48 | |
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| top_k | 0.0 | |
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| top_p | 1.0 | |
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| do_sample | True | |
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| temperature | 1.0 | |
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## Use and Limitations |
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### Intended Use |
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This model is intended to be used for text generation with a focus on conversational tasks. Users may further fine-tune the model on their own data to improve the model's performance on their specific tasks in accordance with the non-commercial [license](https://creativecommons.org/licenses/by-nc/4.0/). |
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### Limitations and bias |
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The base LLaMA model is trained on various data, some of which may contain offensive, harmful, and biased content that can lead to toxic behavior. See Section 5.1 of the LLaMA [paper](https://arxiv.org/abs/2302.13971). We have not performed any studies to determine how fine-tuning on the aforementioned datasets affect the model's behavior and toxicity. Do not treat chat responses from this model as a substitute for human judgment or as a source of truth. Please use responsibly. |
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## Acknowledgements |
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This work would not have been possible without the support of [Stability AI](https://stability.ai/). |
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## Citations |
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```bibtex |
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@article{touvron2023llama, |
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title={LLaMA: Open and Efficient Foundation Language Models}, |
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author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, |
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journal={arXiv preprint arXiv:2302.13971}, |
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year={2023} |
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} |
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``` |
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```bibtex |
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@misc{vicuna2023, |
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title = {Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality}, |
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url = {https://vicuna.lmsys.org}, |
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author = {Chiang, Wei-Lin and Li, Zhuohan and Lin, Zi and Sheng, Ying and Wu, Zhanghao and Zhang, Hao and Zheng, Lianmin and Zhuang, Siyuan and Zhuang, Yonghao and Gonzalez, Joseph E. and Stoica, Ion and Xing, Eric P.}, |
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month = {March}, |
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year = {2023} |
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} |
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``` |
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```bibtex |
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@misc{gpt4all, |
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author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar}, |
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title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/nomic-ai/gpt4all}}, |
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} |
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``` |
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```bibtex |
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@misc{alpaca, |
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author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, |
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title = {Stanford Alpaca: An Instruction-following LLaMA model}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, |
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} |
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``` |
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```bibtex |
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@software{leandro_von_werra_2023_7790115, |
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author = {Leandro von Werra and |
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Alex Havrilla and |
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Max reciprocated and |
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Jonathan Tow and |
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Aman cat-state and |
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Duy V. Phung and |
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Louis Castricato and |
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Shahbuland Matiana and |
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Alan and |
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Ayush Thakur and |
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Alexey Bukhtiyarov and |
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aaronrmm and |
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Fabrizio Milo and |
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Daniel and |
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Daniel King and |
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Dong Shin and |
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Ethan Kim and |
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Justin Wei and |
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Manuel Romero and |
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Nicky Pochinkov and |
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Omar Sanseviero and |
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Reshinth Adithyan and |
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Sherman Siu and |
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Thomas Simonini and |
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Vladimir Blagojevic and |
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Xu Song and |
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Zack Witten and |
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alexandremuzio and |
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crumb}, |
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title = {{CarperAI/trlx: v0.6.0: LLaMa (Alpaca), Benchmark |
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Util, T5 ILQL, Tests}}, |
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month = mar, |
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year = 2023, |
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publisher = {Zenodo}, |
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version = {v0.6.0}, |
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doi = {10.5281/zenodo.7790115}, |
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url = {https://doi.org/10.5281/zenodo.7790115} |
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} |
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``` |
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