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+ ---
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+ base_model: ajibawa-2023/SlimOrca-13B
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+ datasets:
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+ - Open-Orca/SlimOrca
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+ - ajibawa-2023/SlimOrca-ShareGPT
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+ inference: false
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+ language:
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+ - en
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+ license: cc-by-nc-nd-4.0
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+ model_creator: Feynman Innovations
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+ model_name: SlimOrca 13B
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+ model_type: llama
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+ prompt_template: 'This is a conversation with your Assistant. It is a computer program
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+ designed to help you with various tasks such as answering questions, providing recommendations,
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+ and helping with decision making. You can ask it anything you want and it will do
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+ its best to give you accurate and relevant information.
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+
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+
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+ Context
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+
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+ You are a helpful AI assistant.
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+
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+
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+ USER: {prompt}
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+
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+ ASSISTANT:
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+
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+ '
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+ quantized_by: TheBloke
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+ ---
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+ <!-- markdownlint-disable MD041 -->
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+
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+ <!-- header start -->
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+ <!-- 200823 -->
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+ <div style="width: auto; margin-left: auto; margin-right: auto">
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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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's 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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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+ </div>
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+ </div>
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+ <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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+ <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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+ <!-- header end -->
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+
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+ # SlimOrca 13B - AWQ
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+ - Model creator: [Feynman Innovations](https://huggingface.co/ajibawa-2023)
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+ - Original model: [SlimOrca 13B](https://huggingface.co/ajibawa-2023/SlimOrca-13B)
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+
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+ <!-- description start -->
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+ ## Description
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+
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+ This repo contains AWQ model files for [Feynman Innovations's SlimOrca 13B](https://huggingface.co/ajibawa-2023/SlimOrca-13B).
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+
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+ These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
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+
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+
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+ ### About AWQ
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+
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+ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
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+
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+ It is supported by:
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+
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+ - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
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+ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
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+ - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
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+ - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
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+ - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
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+
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+ <!-- description end -->
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+ <!-- repositories-available start -->
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+ ## Repositories available
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+
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+ * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SlimOrca-13B-AWQ)
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+ * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SlimOrca-13B-GPTQ)
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+ * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SlimOrca-13B-GGUF)
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+ * [Feynman Innovations's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ajibawa-2023/SlimOrca-13B)
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+ <!-- repositories-available end -->
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+
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+ <!-- prompt-template start -->
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+ ## Prompt template: Ajibawa-Assistant
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+
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+ ```
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+ This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
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+
90
+ Context
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+ You are a helpful AI assistant.
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+
93
+ USER: {prompt}
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+ ASSISTANT:
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+
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+ ```
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+
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+ <!-- prompt-template end -->
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+ <!-- licensing start -->
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+ ## Licensing
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+
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+ The creator of the source model has listed its license as `cc-by-nc-nd-4.0`, and this quantization has therefore used that same license.
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+
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+ As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
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+
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+ In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Feynman Innovations's SlimOrca 13B](https://huggingface.co/ajibawa-2023/SlimOrca-13B).
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+ <!-- licensing end -->
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+ <!-- README_AWQ.md-provided-files start -->
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+ ## Provided files, and AWQ parameters
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+
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+ I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
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+
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+ Models are released as sharded safetensors files.
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+
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+ | Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
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+ | ------ | ---- | -- | ----------- | ------- | ---- |
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+ | [main](https://huggingface.co/TheBloke/SlimOrca-13B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.25 GB
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+
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+ <!-- README_AWQ.md-provided-files end -->
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+
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+ <!-- README_AWQ.md-text-generation-webui start -->
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+ ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
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+
124
+ Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
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+
126
+ It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
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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/SlimOrca-13B-AWQ`.
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+ 3. Click **Download**.
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+ 4. The model will start downloading. Once it's finished it will say "Done".
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+ 5. In the top left, click the refresh icon next to **Model**.
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+ 6. In the **Model** dropdown, choose the model you just downloaded: `SlimOrca-13B-AWQ`
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+ 7. Select **Loader: AutoAWQ**.
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+ 8. Click Load, and the model will load and is now ready for use.
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+ 9. 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.
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+ 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
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+ <!-- README_AWQ.md-text-generation-webui end -->
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+
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+ <!-- README_AWQ.md-use-from-vllm start -->
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+ ## Multi-user inference server: vLLM
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+
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+ Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
144
+
145
+ - Please ensure you are using vLLM version 0.2 or later.
146
+ - When using vLLM as a server, pass the `--quantization awq` parameter.
147
+
148
+ For example:
149
+
150
+ ```shell
151
+ python3 -m vllm.entrypoints.api_server --model TheBloke/SlimOrca-13B-AWQ --quantization awq --dtype auto
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+ ```
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+
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+ - When using vLLM from Python code, again set `quantization=awq`.
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+
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+ For example:
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+
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+ ```python
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+ from vllm import LLM, SamplingParams
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+
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+ prompts = [
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+ "Tell me about AI",
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+ "Write a story about llamas",
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+ "What is 291 - 150?",
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+ "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
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+ ]
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+ prompt_template=f'''This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
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+
169
+ Context
170
+ You are a helpful AI assistant.
171
+
172
+ USER: {prompt}
173
+ ASSISTANT:
174
+ '''
175
+
176
+ prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
177
+
178
+ sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
179
+
180
+ llm = LLM(model="TheBloke/SlimOrca-13B-AWQ", quantization="awq", dtype="auto")
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+
182
+ outputs = llm.generate(prompts, sampling_params)
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+
184
+ # Print the outputs.
185
+ for output in outputs:
186
+ prompt = output.prompt
187
+ generated_text = output.outputs[0].text
188
+ print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
189
+ ```
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+ <!-- README_AWQ.md-use-from-vllm start -->
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+
192
+ <!-- README_AWQ.md-use-from-tgi start -->
193
+ ## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
194
+
195
+ Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
196
+
197
+ Example Docker parameters:
198
+
199
+ ```shell
200
+ --model-id TheBloke/SlimOrca-13B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
201
+ ```
202
+
203
+ Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
204
+
205
+ ```shell
206
+ pip3 install huggingface-hub
207
+ ```
208
+
209
+ ```python
210
+ from huggingface_hub import InferenceClient
211
+
212
+ endpoint_url = "https://your-endpoint-url-here"
213
+
214
+ prompt = "Tell me about AI"
215
+ prompt_template=f'''This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
216
+
217
+ Context
218
+ You are a helpful AI assistant.
219
+
220
+ USER: {prompt}
221
+ ASSISTANT:
222
+ '''
223
+
224
+ client = InferenceClient(endpoint_url)
225
+ response = client.text_generation(prompt,
226
+ max_new_tokens=128,
227
+ do_sample=True,
228
+ temperature=0.7,
229
+ top_p=0.95,
230
+ top_k=40,
231
+ repetition_penalty=1.1)
232
+
233
+ print(f"Model output: ", response)
234
+ ```
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+ <!-- README_AWQ.md-use-from-tgi end -->
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+
237
+ <!-- README_AWQ.md-use-from-python start -->
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+ ## Inference from Python code using Transformers
239
+
240
+ ### Install the necessary packages
241
+
242
+ - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
243
+ - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
244
+
245
+ ```shell
246
+ pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
247
+ ```
248
+
249
+ Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
250
+
251
+ If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
252
+
253
+ ```shell
254
+ pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
255
+ ```
256
+
257
+ If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
258
+
259
+ ```shell
260
+ pip3 uninstall -y autoawq
261
+ git clone https://github.com/casper-hansen/AutoAWQ
262
+ cd AutoAWQ
263
+ pip3 install .
264
+ ```
265
+
266
+ ### Transformers example code (requires Transformers 4.35.0 and later)
267
+
268
+ ```python
269
+ from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
270
+
271
+ model_name_or_path = "TheBloke/SlimOrca-13B-AWQ"
272
+
273
+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
274
+ model = AutoModelForCausalLM.from_pretrained(
275
+ model_name_or_path,
276
+ low_cpu_mem_usage=True,
277
+ device_map="cuda:0"
278
+ )
279
+
280
+ # Using the text streamer to stream output one token at a time
281
+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
282
+
283
+ prompt = "Tell me about AI"
284
+ prompt_template=f'''This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
285
+
286
+ Context
287
+ You are a helpful AI assistant.
288
+
289
+ USER: {prompt}
290
+ ASSISTANT:
291
+ '''
292
+
293
+ # Convert prompt to tokens
294
+ tokens = tokenizer(
295
+ prompt_template,
296
+ return_tensors='pt'
297
+ ).input_ids.cuda()
298
+
299
+ generation_params = {
300
+ "do_sample": True,
301
+ "temperature": 0.7,
302
+ "top_p": 0.95,
303
+ "top_k": 40,
304
+ "max_new_tokens": 512,
305
+ "repetition_penalty": 1.1
306
+ }
307
+
308
+ # Generate streamed output, visible one token at a time
309
+ generation_output = model.generate(
310
+ tokens,
311
+ streamer=streamer,
312
+ **generation_params
313
+ )
314
+
315
+ # Generation without a streamer, which will include the prompt in the output
316
+ generation_output = model.generate(
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+ tokens,
318
+ **generation_params
319
+ )
320
+
321
+ # Get the tokens from the output, decode them, print them
322
+ token_output = generation_output[0]
323
+ text_output = tokenizer.decode(token_output)
324
+ print("model.generate output: ", text_output)
325
+
326
+ # Inference is also possible via Transformers' pipeline
327
+ from transformers import pipeline
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+
329
+ pipe = pipeline(
330
+ "text-generation",
331
+ model=model,
332
+ tokenizer=tokenizer,
333
+ **generation_params
334
+ )
335
+
336
+ pipe_output = pipe(prompt_template)[0]['generated_text']
337
+ print("pipeline output: ", pipe_output)
338
+
339
+ ```
340
+ <!-- README_AWQ.md-use-from-python end -->
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+
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+ <!-- README_AWQ.md-compatibility start -->
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+ ## Compatibility
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+
345
+ The files provided are tested to work with:
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+
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+ - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
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+ - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
349
+ - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
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+ - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
351
+ - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
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+
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+ <!-- README_AWQ.md-compatibility end -->
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+
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+ <!-- footer start -->
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+ <!-- 200823 -->
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+ ## Discord
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+
359
+ For further support, and discussions on these models and AI in general, join us at:
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+
361
+ [TheBloke AI's Discord server](https://discord.gg/theblokeai)
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+
363
+ ## Thanks, and how to contribute
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+
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+ Thanks to the [chirper.ai](https://chirper.ai) team!
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+
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+ Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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+
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+ 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.
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+
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+ 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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+
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+ Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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+
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+ * Patreon: https://patreon.com/TheBlokeAI
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+ * Ko-Fi: https://ko-fi.com/TheBlokeAI
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+
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+ **Special thanks to**: Aemon Algiz.
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+
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+ **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
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+
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+
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+ Thank you to all my generous patrons and donaters!
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+
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+ And thank you again to a16z for their generous grant.
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+
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+ <!-- footer end -->
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+
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+ # Original model card: Feynman Innovations's SlimOrca 13B
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+
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+
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+ **SlimOrca-13B: A General Purpose Intelligent Model**
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+
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+
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+ This Model is trained on refined version of SlimOrca made available by [Open-Orca](https://huggingface.co/Open-Orca) team.
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+ The idea was to check how this Model will perform in the absence of "system" prompt/instruction.
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+ This Model is very good in various types of General Purpose content generation such as Q&A (including multiple choice), Articles from Summary, Sentiment Analysis,
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+ Context & Hypothesis, Reviews, Erotic story generation etc.
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+ It can also generate Uncensored content. Kindly be careful while generating Uncensored content as you will be responsible for what you
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+ generate.
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+
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+ It is trained on 517981 set of conversations. Each set having 2 conversations. I have shared this [data](https://huggingface.co/datasets/ajibawa-2023/SlimOrca-ShareGPT).
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+
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+ All the credit goes to the Open-Orca team for releasing SlimOrca dataset.
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+
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+
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+ **Training:**
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+ Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took almost 11 Days. DeepSpeed codebase was used for training purpose.
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+ Entire data is trained on Llama-2 by Meta.
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+
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+ This is a full fine tuned model. Links for quantized models are given below.
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+
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+ **GPTQ GGML & AWQ**
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+
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+ GPTQ: TBA
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+
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+ GGUF: TBA
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+
419
+ AWQ: TBA
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+
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+
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+
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+ **Example Prompt:**
424
+ ```
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+ This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.
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+
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+ Context
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+ You are a helpful AI assistant.
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+
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+ USER: <prompt>
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+ ASSISTANT:
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+ ```
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+ You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .
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+
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+
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+ I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
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+
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+ Thank you for your love & support.
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+
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+
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+ **Example Output**
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+
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+ Example 1
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+
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+ ![Example 1](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/hM_EJaSZiMjMQU35EiHGM.png)
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+
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+ Example 2
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+
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+ ![Example 2](https://cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/riNaxJeTWdCEE4dNP8GWp.png)
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+
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+