Initial GPTQ model commit

README.md

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+---
+inference: false
+license: other
+---
+
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+
+# Eric Hartford's Samantha 33B GPTQ
+
+These files are GPTQ 4bit model files for [Eric Hartford's Samantha 33B](https://huggingface.co/ehartford/samantha-1.1-llama-33b) merged with [Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
+
+It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+
+**This is an experimental new GPTQ which offers up to 8K context size**
+
+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).
+
+It has also been tested from Python code using AutoGPTQ, and `trust_remote_code=True`.
+
+Code credits:
+- Original concept and code for increasing context length: [kaiokendev](https://huggingface.co/kaiokendev)
+- Updated Llama modelling code that includes this automatically via trust_remote_code: [emozilla](https://huggingface.co/emozilla).
+
+Please read carefully below to see how to use it.
+
+**NOTE**: Using the full 8K context on a 30B model will exceed 24GB VRAM.
+
+GGML versions are not yet provided, as there is not yet support for SuperHOT in llama.cpp. This is being investigated and will hopefully come soon.
+
+## Repositories available
+
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Samantha-33B-SuperHOT-8K-GPTQ)
+* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Samantha-33B-SuperHOT-8K-fp16)
+* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/samantha-1.1-llama-33b)
+
+## How to easily download and use this model in text-generation-webui with ExLlama
+
+Please make sure you're using the latest version of text-generation-webui
+
+1. Click the **Model tab**.
+2. Under **Download custom model or LoRA**, enter `TheBloke/Samantha-33B-SuperHOT-8K-GPTQ`.
+3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say "Done"
+5. Untick **Autoload the model**
+6. In the top left, click the refresh icon next to **Model**.
+7. In the **Model** dropdown, choose the model you just downloaded: `Samantha-33B-SuperHOT-8K-GPTQ`
+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.
+9. Now click **Save Settings** followed by **Reload**
+10. The model will automatically load, and is now ready for use!
+11. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
+
+## How to use this GPTQ model from Python code with AutoGPTQ
+
+First make sure you have AutoGPTQ and Einops installed:
+
+```
+pip3 install einops auto-gptq
+```
+
+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.
+
+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.
+
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import argparse
+
+model_name_or_path = "TheBloke/Samantha-33B-SuperHOT-8K-GPTQ"
+model_basename = "samantha-33b-superhot-8k-GPTQ-4bit-128g.no-act.order"
+
+use_triton = False
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+
+model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
+        model_basename=model_basename,
+        use_safetensors=True,
+        trust_remote_code=True,
+        device_map='auto',
+        use_triton=use_triton,
+        quantize_config=None)
+
+model.seqlen = 8192
+
+# Note: check the prompt template is correct for this model.
+prompt = "Tell me about AI"
+prompt_template=f'''USER: {prompt}
+ASSISTANT:'''
+
+print("\n\n*** Generate:")
+
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+
+# Inference can also be done using transformers' pipeline
+
+# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
+logging.set_verbosity(logging.CRITICAL)
+
+print("*** Pipeline:")
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.95,
+    repetition_penalty=1.15
+)
+
+print(pipe(prompt_template)[0]['generated_text'])
+```
+
+## Using other UIs: monkey patch
+
+Provided in the repo is `llama_rope_scaled_monkey_patch.py`, written by @kaiokendev.
+
+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.
+
+## Provided files
+
+**samantha-33b-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors**
+
+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.
+
+It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.
+
+* `samantha-33b-superhot-8k-GPTQ-4bit-128g.no-act.order.safetensors`
+  * 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.
+  * Parameters: Groupsize = 128. Act Order / desc_act = False.
+
+<!-- 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, Dmitriy Samsonov.
+
+**Patreon special mentions**: Pyrater, WelcomeToTheClub, Kalila, Mano Prime, Trenton Dambrowitz, Spiking Neurons AB, Pierre Kircher, Fen Risland, Kevin Schuppel, Luke, Rainer Wilmers, vamX, Gabriel Puliatti, Alex , Karl Bernard, Ajan Kanaga, Talal Aujan, Space Cruiser, ya boyyy, biorpg, Johann-Peter Hartmann, Asp the Wyvern, Ai Maven, Ghost , Preetika Verma, Nikolai Manek, trip7s trip, John Detwiler, Fred von Graf, Artur Olbinski, subjectnull, John Villwock, Junyu Yang, Rod A, Lone Striker, Chris McCloskey, Iucharbius , Matthew Berman, Illia Dulskyi, Khalefa Al-Ahmad, Imad Khwaja, chris gileta, Willem Michiel, Greatston Gnanesh, Derek Yates, K, Alps Aficionado, Oscar Rangel, David Flickinger, Luke Pendergrass, Deep Realms, Eugene Pentland, Cory Kujawski, terasurfer , Jonathan Leane, senxiiz, Joseph William Delisle, Sean Connelly, webtim, zynix , Nathan LeClaire.
+
+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
+
+This is a second prototype of SuperHOT, this time 30B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
+Tests have shown that the model does indeed leverage the extended context at 8K.
+
+You will need to **use either the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
+
+#### Looking for Merged & Quantized Models?
+- 30B 4-bit CUDA: [tmpupload/superhot-30b-8k-4bit-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-safetensors)
+- 30B 4-bit CUDA 128g: [tmpupload/superhot-30b-8k-4bit-128g-safetensors](https://huggingface.co/tmpupload/superhot-30b-8k-4bit-128g-safetensors)
+
+
+#### Training Details
+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
+
+# Original model card: Eric Hartford's Samantha 33B
+
+No original model card was provided.