Update README.md

README.md

@@ -1,73 +1,32 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- text-generation-inference
-- R1
-- Qwen
-- Deepseek
----
-![ASFSADFCS.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/U8o3r3pREezkHYVAEEcqc.png)
-
-# **Elita-0.1-Distilled-R1-Abliterated**  
-
-Elita-0.1-Distilled-R1-Abliterated is based on the *Qwen [ KT ] model*, which was distilled by *DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B*. It has been fine-tuned on the long chain-of-thought reasoning model and specialized datasets, focusing on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.  
-
-# **Quickstart with Transformers**  
-
-Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.  
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Elita-0.1-Distilled-R1-Abliterated"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to large language model."
-messages = [
-    {"role": "system", "content": "You are Elita, created by DeepSeek-AI. You are a powerful reasoning assistant."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-
-### **Intended Use:**  
-1. **Instruction-Following:** The model excels in understanding and executing detailed instructions, making it ideal for automation systems, virtual assistants, and educational tools.  
-2. **Text Generation:** It can produce coherent, logically structured, and contextually relevant text for use in content creation, summarization, and report writing.  
-3. **Complex Reasoning Tasks:** With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks.  
-4. **Research and Development:** It can support researchers and developers in exploring advancements in logical reasoning and fine-tuning methodologies.  
-5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving by generating step-by-step solutions.  
-
-### **Limitations:**  
-1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains.  
-2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.  
-3. **Bias in Training Data:** The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts.  
-4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.  
-5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.  
-6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.  
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Elita-0.1-Distilled-R1-abliterated
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- R1
+- Qwen
+- Deepseek
+---
+# Melvin56/Elita-0.1-Distilled-R1-abliterated-GGUF
+
+Original Model : [prithivMLmods/Elita-0.1-Distilled-R1-abliterated](https://huggingface.co/prithivMLmods/Elita-0.1-Distilled-R1-abliterated)
+
+All quants are made using the imatrix option.
+
+| Model                                            |   Size (GB)   |
+|:-------------------------------------------------|:-------------:|
+| Q2_K_S |     2.82    |
+| Q2_K |     3.01    |
+| Q3_K_M |     3.80    |
+| Q3_K_L |     4.08    |
+| Q4_K_S   |     4.46    |
+| Q4_K_M |     4.68    |
+| Q5_K_S   |     5.30    |
+| Q5_K_M |     5.44    |
+| Q6_K   |     6.25    |
+| Q8_0   |     8.10    |
+| F16    |    15.24    |