Update README.md

README.md

@@ -23,10 +23,17 @@ These files are GPTQ 4bit model files for [Panchovix's merge of Guanaco 33B and
 
 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 currently only 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).
+
+Please read carefully below to see how to use it.
+
+**NOTE**: Using the full 8K context will exceed 24GB VRAM.
+
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Guanaco-33B-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/Panchovix/Guanaco-33B-SuperHOT-8k)
 
 ## How to easily download and use this model in text-generation-webui
@@ -37,70 +44,17 @@ Please make sure you're using the latest version of text-generation-webui
 2. Under **Download custom model or LoRA**, enter `TheBloke/Guanaco-33B-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: `Guanaco-33B-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/Guanaco-33B-SuperHOT-8K-GPTQ"
-model_basename = "guanaco-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=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)
+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: `Guanaco-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!
 
-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
-)
+## How to use this GPTQ model from Python code - TBC
 
-print(pipe(prompt_template)[0]['generated_text'])
-```
+Using this model with increased context from Python code is currently untested, so this section is removed for now.
 
 ## Provided files
 
@@ -111,9 +65,9 @@ This will work with AutoGPTQ, ExLlama, and CUDA versions of GPTQ-for-LLaMa. Ther
 It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.
 
 * `guanaco-33b-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.
+  * Designed for use with ExLlama with increased context (4096 or 8192)
+  * Should work with AutoGPTQ in CUDA or Triton modes, but without increased context - TBC.
+  * Should work 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.