Add processing notice

README.md

@@ -1,70 +1,13 @@
 ---
-base_model: rombodawg/Llama-3-8B-Base-Coder-v3.5-10k
 inference: false
-library_name: transformers
-pipeline_tag: text-generation
-quantized_by: Suparious
-tags:
-- 4-bit
-- AWQ
-- text-generation
-- autotrain_compatible
-- endpoints_compatible
 ---
 # rombodawg/Llama-3-8B-Base-Coder-v3.5-10k AWQ
 
+** PROCESSING .... ETA 30mins **
+
 - Model creator: [rombodawg](https://huggingface.co/rombodawg)
 - Original model: [Llama-3-8B-Base-Coder-v3.5-10k](https://huggingface.co/rombodawg/Llama-3-8B-Base-Coder-v3.5-10k)
 
-
-
-## 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/Llama-3-8B-Base-Coder-v3.5-10k-AWQ"
-system_message = "You are Llama-3-8B-Base-Coder-v3.5-10k, incarnated as a powerful AI. You were created by rombodawg."
-
-# 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.