Initial GPTQ model commit

README.md

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+---
+inference: false
+license: other
+---
+
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+
+# Minlik's Chinese Alpaca 33B Merged GPTQ
+
+These files are GPTQ 4bit model files for [Minlik's Chinese Alpaca 33B Merged](https://huggingface.co/minlik/chinese-alpaca-33b-merged) 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.
+
+## Repositories available
+
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-GGML)
+* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-fp16)
+* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/minlik/chinese-alpaca-33b-merged)
+
+## 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/Chinese-Alpaca-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: `Chinese-Alpaca-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/Chinese-Alpaca-33B-SuperHOT-8K-GPTQ"
+model_basename = "chinese-alpaca-33b-superhot-8k-GPTQ-4bit--1g.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
+
+**chinese-alpaca-33b-superhot-8k-GPTQ-4bit--1g.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 without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.
+
+* `chinese-alpaca-33b-superhot-8k-GPTQ-4bit--1g.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 = -1. Act Order / desc_act = True.
+
+<!-- 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**: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.
+
+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: Minlik's Chinese Alpaca 33B Merged
+
+
+加入��文词表并继续预训练中文Embedding，并在此基础上继续使用指令数据集finetuning，得到的中文Alpaca-33B模型。
+
+模型转换用到的相关base及lora模型如下：
+- base-model: elinas/llama-30b-hf-transformers-4.29
+- lora-model: ziqingyang/chinese-alpaca-lora-33b
+
+详情可参考：https://github.com/ymcui/Chinese-LLaMA-Alpaca/releases/tag/v4.0
+
+
+### 使用方法参考
+1. 安装模块包
+```bash
+pip install sentencepiece
+pip install transformers>=4.28.0
+```
+
+2. 生成文本
+```python
+import torch
+import transformers
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+def generate_prompt(text):
+    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{text}
+
+### Response:"""
+
+
+tokenizer = LlamaTokenizer.from_pretrained('minlik/chinese-alpaca-33b-merged')
+model = LlamaForCausalLM.from_pretrained('minlik/chinese-alpaca-33b-merged').half().to('cuda')
+model.eval()
+
+text = '第一个登上月球的人是谁？'
+prompt = generate_prompt(text)
+input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
+
+
+with torch.no_grad():
+    output_ids = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=128,
+        temperature=1,
+        top_k=40,
+        top_p=0.9,
+        repetition_penalty=1.15
+    ).cuda()
+output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(output.replace(prompt, '').strip())
+```