|
--- |
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inference: false |
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license: other |
|
--- |
|
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<!-- header start --> |
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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/theblokeai">Chat & support: my new Discord server</a></p> |
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</div> |
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<p><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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<!-- header end --> |
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|
|
# Allen AI's Tulu 7B GPTQ |
|
|
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These files are GPTQ 4bit model files for [Allen AI's Tulu 7B](https://huggingface.co/TheBloke/tulu-7B-fp16) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-7b-8k-no-rlhf-test). |
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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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**This is an experimental new GPTQ which offers up to 8K context size** |
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The increased context is tested to work with [ExLlama](https://github.com/turboderp/exllama), via the latest release of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). |
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It has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`. |
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Code credits: |
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- Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev) |
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- Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla). |
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Please read carefully below to see how to use it. |
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|
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## Repositories available |
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|
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Tulu-7B-SuperHOT-8K-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Tulu-7B-SuperHOT-8K-GGML) |
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* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Tulu-7B-SuperHOT-8K-fp16) |
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* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/allenai/tulu-7b) |
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|
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## How to easily download and use this model in text-generation-webui with ExLlama |
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Please make sure you're using the latest version of text-generation-webui |
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1. Click the **Model tab**. |
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2. Under **Download custom model or LoRA**, enter `TheBloke/Tulu-7B-SuperHOT-8K-GPTQ`. |
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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. Untick **Autoload the model** |
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6. In the top left, click the refresh icon next to **Model**. |
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7. In the **Model** dropdown, choose the model you just downloaded: `Tulu-7B-SuperHOT-8K-GPTQ` |
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8. To use the increased context, set the **Loader** to **ExLlama**, set **max_seq_len** to 8192 or 4096, and set **compress_pos_emb** to **4** for 8192 context, or to **2** for 4096 context. |
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9. Now click **Save Settings** followed by **Reload** |
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10. The model will automatically load, and is now ready for use! |
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11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! |
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|
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## How to use this GPTQ model from Python code with AutoGPTQ |
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First make sure you have AutoGPTQ and Einops installed: |
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``` |
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pip3 install einops auto-gptq |
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``` |
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Then run the following code. Note that in order to get this to work, `config.json` has been hardcoded to a sequence length of 8192. |
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If you want to try 4096 instead to reduce VRAM usage, please manually edit `config.json` to set `max_position_embeddings` to the value you want. |
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```python |
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from transformers import AutoTokenizer, pipeline, logging |
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
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import argparse |
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model_name_or_path = "TheBloke/Tulu-7B-SuperHOT-8K-GPTQ" |
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model_basename = "tulu-7b-superhot-8k-GPTQ-4bit-128g.no-act.order" |
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use_triton = False |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) |
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model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, |
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model_basename=model_basename, |
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use_safetensors=True, |
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trust_remote_code=True, |
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device_map='auto', |
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use_triton=use_triton, |
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quantize_config=None) |
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model.seqlen = 8192 |
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|
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# Note: check the prompt template is correct for this model. |
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prompt = "Tell me about AI" |
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prompt_template=f'''USER: {prompt} |
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ASSISTANT:''' |
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print("\n\n*** Generate:") |
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input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() |
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output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) |
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print(tokenizer.decode(output[0])) |
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|
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# Inference can also be done using transformers' pipeline |
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|
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# Prevent printing spurious transformers error when using pipeline with AutoGPTQ |
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logging.set_verbosity(logging.CRITICAL) |
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print("*** Pipeline:") |
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pipe = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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max_new_tokens=512, |
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temperature=0.7, |
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top_p=0.95, |
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repetition_penalty=1.15 |
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) |
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print(pipe(prompt_template)[0]['generated_text']) |
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``` |
|
|
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## Using other UIs: monkey patch |
|
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Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev. |
|
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|
It can be theoretically be added to any Python UI or custom code to enable the same result as `trust_remote_code=True`. I have not tested this, and it should be superseded by using `trust_remote_code=True`, but I include it for completeness and for interest. |
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|
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## Provided files |
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**tulu-7b-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors** |
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This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead. |
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It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed. |
|
|
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* `tulu-7b-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors` |
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* Works for use with ExLlama with increased context (4096 or 8192) |
|
* Works with AutoGPTQ in Python code, including with increased context, if `trust_remote_code=True` is set. |
|
* Should work with GPTQ-for-LLaMa in CUDA mode, but unknown if increased context works - TBC. May have issues with GPTQ-for-LLaMa Triton mode. |
|
* Works with text-generation-webui, including one-click-installers. |
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* Parameters: Groupsize = 128. Act Order / desc_act = False. |
|
|
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<!-- footer start --> |
|
## Discord |
|
|
|
For further support, and discussions on these models and AI in general, join us at: |
|
|
|
[TheBloke AI's Discord server](https://discord.gg/theblokeai) |
|
|
|
## Thanks, and how to contribute. |
|
|
|
Thanks to the [chirper.ai](https://chirper.ai) team! |
|
|
|
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. |
|
|
|
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
|
|
|
* Patreon: https://patreon.com/TheBlokeAI |
|
* Ko-Fi: https://ko-fi.com/TheBlokeAI |
|
|
|
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz. |
|
|
|
**Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi. |
|
|
|
Thank you to all my generous patrons and donaters! |
|
|
|
<!-- footer end --> |
|
|
|
# Original model card: Kaio Ken's SuperHOT 8K |
|
|
|
|
|
### SuperHOT Prototype 2 w/ 8K Context |
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This is a second prototype of SuperHOT, a NSFW focused LoRA, this time 7B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k). |
|
|
|
#### Looking for Merged & Quantized Models? |
|
Make some please :) |
|
|
|
#### Using the monkey-patch? |
|
You will **NEED** to **apply the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192** |
|
|
|
The monkeypatch is only necessary if you are using a front-end/back-end that does not already support scaling and said front-end/back-end is Python-based (i.e. Huggingface Transformers). To apply the patch, you will need to copy the `llama_rope_scaled_monkey_patch.py` into your working directory and call the exported function `replace_llama_rope_with_scaled_rope` at the very start of your Python program. It will modify the Transformers library's implementation of RoPE to properly apply the scaling factor. |
|
|
|
#### Using Oobabooga with Exllama? |
|
Switch your loader to `exllama` or `exllama_hf` Add the arguments `max_seq_len 8192` and `compress_pos_emb 4`. **While the model may work well with `compress_pos_emb 2`, it was trained on 4, so that is what I advocate for you to use** |
|
|
|
Example in the command-line: |
|
- `python server.py --max_seq_len 8192 --compress_pos_emb 4 --loader exllama_hf` |
|
|
|
In the UI, you will see the loader option in the `Models` tab. Once you select either `exllama` or `exllama_hf`, the `max_seq_len` and `compress_pos_emb` settings will appear. |
|
|
|
#### Training Details |
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I trained the LoRA with the following configuration: |
|
- 1200 samples (~400 samples over 2048 sequence length) |
|
- learning rate of 3e-4 |
|
- 3 epochs |
|
- The exported modules are: |
|
- q_proj |
|
- k_proj |
|
- v_proj |
|
- o_proj |
|
- no bias |
|
- Rank = 4 |
|
- Alpha = 8 |
|
- no dropout |
|
- weight decay of 0.1 |
|
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5 |
|
- Trained on 4-bit base model |
|
- Cutoff length: 4096 |
|
|
|
# Original model card: Allen AI's Tulu 7B |
|
|
|
|
|
<!-- header start --> |
|
<div style="width: 100%;"> |
|
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
|
</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
|
<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
|
<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p> |
|
</div> |
|
<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
|
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> |
|
</div> |
|
</div> |
|
<!-- header end --> |
|
|
|
# Allen AI's Tulu 7B fp16 |
|
|
|
These files are pytorch format fp16 model files for [Allen AI's Tulu 7B](https://huggingface.co/allenai/tulu-7b). |
|
|
|
It is the result of merging and/or converting the source repository to float16. |
|
|
|
## Repositories available |
|
|
|
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/tulu-7B-fp16) |
|
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/tulu-7B-GGML) |
|
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/tulu-7B-fp16) |
|
|
|
## Prompt template |
|
|
|
The following template should be used: |
|
|
|
``` |
|
<|user|> |
|
prompt goes here |
|
<|assistant|> |
|
|
|
``` |
|
|
|
**Note**: There should be a newline after `<|assistant|>`. This appears to be very important for getting this model to respond correctly. |
|
|
|
In other words, the prompt is: |
|
|
|
``` |
|
<|user|>\nprompt goes here\n<|assistant|>\n |
|
``` |
|
|
|
<!-- footer start --> |
|
## Discord |
|
|
|
For further support, and discussions on these models and AI in general, join us at: |
|
|
|
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) |
|
|
|
## Thanks, and how to contribute. |
|
|
|
Thanks to the [chirper.ai](https://chirper.ai) team! |
|
|
|
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. |
|
|
|
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
|
|
|
* Patreon: https://patreon.com/TheBlokeAI |
|
* Ko-Fi: https://ko-fi.com/TheBlokeAI |
|
|
|
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov. |
|
|
|
**Patreon special mentions**: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi. |
|
|
|
Thank you to all my generous patrons and donaters! |
|
|
|
<!-- footer end --> |
|
|
|
# Original model card: Allen AI's Tulu 7B |
|
|
|
|
|
# Tulu 7B |
|
|
|
This model is a 7B LLaMa model finetuned on a mixture of instruction datasets (FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT). |
|
*Please note this is a model diff - see below for usage instructions*. |
|
|
|
This was trained as part of the paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751). |
|
The codebase used to train and evaluate this model can be found at [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct). |
|
|
|
This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt). |
|
|
|
## Usage |
|
|
|
We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here: |
|
[https://huggingface.co/docs/transformers/main/model_doc/llama](https://huggingface.co/docs/transformers/main/model_doc/llama) |
|
|
|
Clone [https://github.com/allenai/open-instruct](https://github.com/allenai/open-instruct) and install the required dependencies, or just copy `scripts/weight_diff.py` |
|
and install the minimal requirements listed in `weight-diff-requirements.txt`. Then download or clone this model diff to the same machine. |
|
|
|
Then, run: |
|
```bash |
|
python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location} |
|
``` |
|
|
|
And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models. |
|
|
|
## Input Format |
|
|
|
The model is trained to use the following format (note the newlines): |
|
``` |
|
<|user|> |
|
Your message here! |
|
<|assistant|> |
|
``` |
|
|
|
For best results, format all inputs in this manner. |
|
|
|
## Performance |
|
|
|
Here is the performance of this model across benchmarks explored in our paper [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://arxiv.org/abs/2306.04751): |
|
|
|
| MMLU 0-shot | MMLU 5-shot | GSM Direct | GSM CoT | BBH Direct | BBH CoT | TydiQA Gold-Passage | TydiQA Closed-book | Codex-Eval Pass@1 | Codex-Eval Pass@10 | AlpacaFarm vs Davinci-003 | Average | |
|
|:-----------:|:-----------:|:----------:|:-------:|:----------:|:-------:|:-------------------:|:------------------:|:-----------------:|:------------------:|:-------------------------:|---------| |
|
| 44.5 | 47.0 | 6.0 | 27.0 | 38.1 | 39.2 | 45.7 | 7.7 | 17.5 | 27.8 | 48.3 | 33.1 | |
|
|
|
If you use this model, please cite our work, the llama paper, and the original datasets: |
|
|
|
``` |
|
@misc{wang2023far, |
|
title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, |
|
author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi}, |
|
year={2023}, |
|
eprint={2306.04751}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
``` |
|
@misc{touvron2023llama, |
|
title={LLaMA: Open and Efficient Foundation Language Models}, |
|
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample}, |
|
year={2023}, |
|
eprint={2302.13971}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
``` |
|
@misc{dolly, |
|
author = {Databricks}, |
|
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, |
|
year = {2023}, |
|
publisher = {GitHub}, |
|
journal = {GitHub repository}, |
|
howpublished = {Blog post}, |
|
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm} |
|
} |
|
``` |
|
|
|
``` |
|
@article{longpre2023flan, |
|
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, |
|
author={Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V and Zoph, Barret and Wei, Jason and others}, |
|
journal={arXiv preprint arXiv:2301.13688}, |
|
year={2023} |
|
} |
|
``` |
|
|
|
``` |
|
@misc{köpf2023openassistant, |
|
title={OpenAssistant Conversations -- Democratizing Large Language Model Alignment}, |
|
author={Andreas Köpf and Yannic Kilcher and Dimitri von Rütte and Sotiris Anagnostidis and Zhi-Rui Tam and Keith Stevens and Abdullah Barhoum and Nguyen Minh Duc and Oliver Stanley and Richárd Nagyfi and Shahul ES and Sameer Suri and David Glushkov and Arnav Dantuluri and Andrew Maguire and Christoph Schuhmann and Huu Nguyen and Alexander Mattick}, |
|
year={2023}, |
|
eprint={2304.07327}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
``` |
|
@article{peng2023instruction, |
|
title={Instruction Tuning with GPT-4}, |
|
author={Peng, Baolin and Li, Chunyuan and He, Pengcheng and Galley, Michel and Gao, Jianfeng}, |
|
journal={arXiv preprint arXiv:2304.03277}, |
|
year={2023} |
|
} |
|
``` |
|
|
|
``` |
|
@misc{codealpaca, |
|
author = {Sahil Chaudhary}, |
|
title = {Code Alpaca: An Instruction-following LLaMA model for code generation}, |
|
year = {2023}, |
|
publisher = {GitHub}, |
|
journal = {GitHub repository}, |
|
howpublished = {\url{https://github.com/sahil280114/codealpaca}}, |
|
} |
|
``` |
|
|
