Add processing notice

README.md

@@ -1,119 +1,13 @@
 ---
-base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
-library_name: transformers
-language:
-- en
-license: apache-2.0
-tags:
-- generated_from_trainer
-- quantized
-- 4-bit
-- AWQ
-- autotrain_compatible
-- endpoints_compatible
-- text-generation-inference
-- chatml
-datasets:
-- cognitivecomputations/dolphin
-- cognitivecomputations/dolphin-coder
-- cognitivecomputations/samantha-data
-- jondurbin/airoboros-2.2.1
-- teknium/openhermes-2.5
-- m-a-p/Code-Feedback
-- m-a-p/CodeFeedback-Filtered-Instruction
-model-index:
-- name: workspace/dolphin-2.8-mistral-7b
-  results: []
-quantized_by: Suparious
-pipeline_tag: text-generation
-model_creator: cognitivecomputations
-model_name: dolphin-2.8-mistral-7b-v02
-model_type: mistral
 inference: false
-prompt_template: '<|im_start|>system
-
-  {system_message}<|im_end|>
-
-  <|im_start|>user
-
-  {prompt}<|im_end|>
-
-  <|im_start|>assistant
-
-  '
 ---
-# cognitivecomputations/dolphin-2.8-mistral-7b-v02 🐬 AWQ
+# cognitivecomputations/dolphin-2.8-mistral-7b-v02 AWQ
+
+** PROCESSING .... ETA 30mins **
 
 - Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
 - Original model: [dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)
 
-<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
-
-## Model Summary
-
-My appreciation for the sponsors of Dolphin 2.8:
-- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
-- [Winston Sou](https://twitter.com/WinsonDabbles) - Along with a generous anonymous sponsor, donated a massive personally owned compute resource!
-- [Abacus AI](https://abacus.ai/) - my employer and partner in many things.
-
-This model is based on [Mistral-7b-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace.  Thanks to @alpindale for converting / publishing.
-
-The base model has 32k context, and the full-weights fine-tune was with 16k sequence lengths.
-
-It took 3 days on 10x L40S provided by [Crusoe Cloud](https://crusoe.ai/)
-
-Dolphin-2.8 has a variety of instruction, conversational, and coding skills.
-
-This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
-
-## How to use
-
-### Install the necessary packages
-
-```bash
-pip install --upgrade autoawq autoawq-kernels
-```
-
-### Example Python code
-
-```python
-from awq import AutoAWQForCausalLM
-from transformers import AutoTokenizer, TextStreamer
-
-model_path = "solidrust/dolphin-2.8-mistral-7b-v02-AWQ"
-system_message = "You are Dolphin, incarnated as a powerful AI."
-
-# Load model
-model = AutoAWQForCausalLM.from_quantized(model_path,
-                                          fuse_layers=True)
-tokenizer = AutoTokenizer.from_pretrained(model_path,
-                                          trust_remote_code=True)
-streamer = TextStreamer(tokenizer,
-                        skip_prompt=True,
-                        skip_special_tokens=True)
-
-# Convert prompt to tokens
-prompt_template = """\
-<|im_start|>system
-{system_message}<|im_end|>
-<|im_start|>user
-{prompt}<|im_end|>
-<|im_start|>assistant"""
-
-prompt = "You're standing on the surface of the Earth. "\
-        "You walk one mile south, one mile west and one mile north. "\
-        "You end up exactly where you started. Where are you?"
-
-tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
-                  return_tensors='pt').input_ids.cuda()
-
-# Generate output
-generation_output = model.generate(tokens,
-                                  streamer=streamer,
-                                  max_new_tokens=512)
-
-```
-
 ### About AWQ
 
 AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
@@ -127,13 +21,3 @@ It is supported by:
 - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
 - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
 - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
-
-## Prompt template: ChatML
-
-```plaintext
-<|im_start|>system
-{system_message}<|im_end|>
-<|im_start|>user
-{prompt}<|im_end|>
-<|im_start|>assistant
-```