Add processing notice

README.md

@@ -1,91 +1,13 @@
 ---
-base_model: mlabonne/ChimeraLlama-3-8B-v3
 inference: false
-library_name: transformers
-license: other
-merged_models:
-- NousResearch/Meta-Llama-3-8B-Instruct
-- mlabonne/OrpoLlama-3-8B
-- cognitivecomputations/dolphin-2.9-llama3-8b
-- Danielbrdz/Barcenas-Llama3-8b-ORPO
-- VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
-- vicgalle/Configurable-Llama-3-8B-v0.3
-- MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
-pipeline_tag: text-generation
-quantized_by: Suparious
-tags:
-- 4-bit
-- AWQ
-- text-generation
-- autotrain_compatible
-- endpoints_compatible
-- merge
-- mergekit
-- lazymergekit
 ---
 # mlabonne/ChimeraLlama-3-8B-v3 AWQ
 
+** PROCESSING .... ETA 30mins **
+
 - Model creator: [mlabonne](https://huggingface.co/mlabonne)
 - Original model: [ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
 
-## Model Summary
-
-ChimeraLlama-3-8B-v3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
-* [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B)
-* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
-* [Danielbrdz/Barcenas-Llama3-8b-ORPO](https://huggingface.co/Danielbrdz/Barcenas-Llama3-8b-ORPO)
-* [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
-* [vicgalle/Configurable-Llama-3-8B-v0.3](https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.3)
-* [MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3)
-
-## 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/ChimeraLlama-3-8B-v3-AWQ"
-system_message = "You are ChimeraLlama-3-8B-v3, incarnated as a powerful AI. You were created by mlabonne."
-
-# 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.