Create README.md

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+base_model: Bin12345/AutoCoder_S_6.7B
+---
+
+# QuantFactory/AutoCoder_S_6.7B-GGUF
+This is quantized version of [Bin12345/AutoCoder_S_6.7B](https://huggingface.co/Bin12345/AutoCoder_S_6.7B) created using llama.cpp
+
+# Model Description
+
+We introduced a new model designed for the Code generation task. It 33B version's test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).
+
+Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can **automatically install the required packages** and attempt to run the code until it deems there are no issues, **whenever the user wishes to execute the code**.
+
+This is the 6.7B version of AutoCoder. Its base model is deepseeker-coder.
+
+See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder).
+
+Simple test script:
+
+```
+model_path = ""
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, 
+                                             device_map="auto")
+
+HumanEval = load_dataset("evalplus/humanevalplus")
+
+Input = "" # input your question here
+ 
+messages=[
+    { 'role': 'user', 'content': Input}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, 
+                                        return_tensors="pt").to(model.device)
+
+outputs = model.generate(inputs, 
+                        max_new_tokens=1024, 
+                        do_sample=False, 
+                        temperature=0.0,
+                        top_p=1.0, 
+                        num_return_sequences=1, 
+                        eos_token_id=tokenizer.eos_token_id)
+
+answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+```
+
+Paper: https://arxiv.org/abs/2405.14906