Text Generation
Transformers
llama
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
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+ inference: false
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+ ---
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+
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+ # WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct
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+
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+ These files are GPTQ 4bit model files for [Eric Hartford's 'uncensored' version of WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored).
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+
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+ It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
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+
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+ Eric did a fresh 7B training using the WizardLM method, on [a dataset edited to remove all the "I'm sorry.." type ChatGPT responses](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered).
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+
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+ ## Other repositories available
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+
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+ * [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)
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+ * [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML)
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+ * [Eric's unquantised model in HF format](https://huggingface.co/ehartford/WizardLM-7B-Uncensored)
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+
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+ ## How to easily download and use this model in text-generation-webui
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+
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+ Open the text-generation-webui UI as normal.
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+
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+ 1. Click the **Model tab**.
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+ 2. Under **Download custom model or LoRA**, enter `TheBloke/WizardLM-7B-uncensored-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, `WizardLM-7B-uncensored-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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+
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+ ## Provided files
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+
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+ **Compatible file - WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors**
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+
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+ In the `main` branch - the default one - you will find `WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors`
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+
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+ This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility
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+
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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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+
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+ * `wizard-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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+ python llama.py models/ehartford_WizardLM-7B-Uncensored c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/eric-gptq/WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors
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+ ```
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+
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+ # Eric's original model card
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+
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+ This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
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+
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+ Shout out to the open source AI/ML community, and everyone who helped me out, including Rohan, TheBloke, and Caseus
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+
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+ # WizardLM's original model card
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+
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+ Overview of Evol-Instruct
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+ Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.
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+
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+ ![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_overall.png)
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+ ![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_running.png)
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+ url: https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
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+ branch: main
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+ download date: 2023-05-07 14:48:18
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+ sha256sum:
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