Initial GPTQ model commit

README.md

@@ -17,18 +17,106 @@ license: other
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-# Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K fp16
+# Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K GPTQ
 
-These files are pytorch format fp16 model files for [Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
+These files are GPTQ 4bit model files for [Eric Hartford's Wizard Vicuna 30B Uncensored merged with Kaio Ken's SuperHOT 8K](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test).
 
-It is the result of merging and/or converting the source repository to float16.
+It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
 
 ## Repositories available
 
-* [4-bit GPTQ models for GPU inference](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored)
+* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/none)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored)
 
+## How to easily download and use this model in text-generation-webui
+
+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/Wizard-Vicuna-30B-Superhot-8K-GPTQ`.
+3. Click **Download**.
+4. The model will start downloading. Once it's finished it will say "Done"
+5. In the top left, click the refresh icon next to **Model**.
+6. In the **Model** dropdown, choose the model you just downloaded: `Wizard-Vicuna-30B-Superhot-8K-GPTQ`
+7. The model will automatically load, and is now ready for use!
+8. 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.
+  * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
+9. 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
+
+First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
+
+`pip install auto-gptq`
+
+Then try the following example code:
+
+```python
+from transformers import AutoTokenizer, pipeline, logging
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import argparse
+
+model_name_or_path = "TheBloke/Wizard-Vicuna-30B-Superhot-8K-GPTQ"
+model_basename = "wizard-vicuna-30b-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=False,
+        device="cuda:0",
+        use_triton=use_triton,
+        quantize_config=None)
+
+# 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'])
+```
+
+## Provided files
+
+**wizard-vicuna-30b-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.
+
+* `wizard-vicuna-30b-superhot-8k-GPTQ-4bit--1g.act.order.safetensors`
+  * Works with AutoGPTQ in CUDA or Triton modes.
+  * LLaMa models also work with [ExLlama](https://github.com/turboderp/exllama}, which usually provides much higher performance, and uses less VRAM, than AutoGPTQ.
+  * Works with GPTQ-for-LLaMa in CUDA mode.  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.
+
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