Update README.md

README.md

@@ -18,6 +18,166 @@ tags:
 This model was converted to GGUF format from [`prithivMLmods/LwQ-10B-Instruct`](https://huggingface.co/prithivMLmods/LwQ-10B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/LwQ-10B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+LwQ-10B-Instruct (Llama with Questions), based on the Llama 3.1 
+collection of multilingual large language models (LLMs), is a set of 
+pre-trained and instruction-tuned generative models optimized for 
+multilingual dialogue use cases. These models outperform many available 
+open-source alternatives. Model Architecture: Llama 3.1 is an 
+auto-regressive language model that utilizes an optimized transformer 
+architecture. The tuned versions undergo supervised fine-tuning (SFT) 
+and reinforcement learning with human feedback (RLHF) to better align 
+with human preferences for helpfulness and safety. LwQ-10B is trained on
+ synthetic reasoning datasets for mathematical reasoning and 
+context-based problem-solving, with a focus on following instructions or
+ keywords embedded in the input.
+
+
+
+	
+		
+	
+
+		Use with transformers
+	
+
+
+
+Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+
+import transformers
+import torch
+
+model_id = "prithivMLmods/LwQ-10B-Instruct"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+
+
+
+
+	
+		
+	
+
+		Intended Use
+	
+
+
+
+Multilingual Conversational Agents:
+LwQ-10B-Instruct
+ is well-suited for building multilingual chatbots and virtual 
+assistants, providing accurate and context-aware responses in various 
+languages.  
+
+
+Instruction-Following Applications:
+The model
+ is ideal for tasks where adherence to specific instructions is 
+critical, such as task automation, guided workflows, and structured 
+content generation.  
+
+
+Mathematical and Logical Reasoning:
+Trained 
+on synthetic reasoning datasets, LwQ-10B can handle mathematical 
+problem-solving, logical reasoning, and step-by-step explanations, 
+making it suitable for education platforms and tutoring systems.  
+
+
+Contextual Problem-Solving:
+The model is 
+optimized for solving contextually rich problems by understanding and 
+processing inputs with embedded instructions or keywords, useful for 
+complex decision-making and recommendation systems.  
+
+
+Content Creation and Summarization:
+LwQ-10B can generate high-quality content, including articles, reports, and summaries, across different languages and domains.
+
+
+
+
+
+	
+		
+	
+
+		Limitations
+	
+
+
+
+Limited Context Window:
+The model has a 
+finite context length, which may affect its ability to handle tasks 
+requiring extensive context or long conversations effectively.  
+
+
+Performance Variability Across Languages:
+While
+ it supports multiple languages, performance may vary, with higher 
+accuracy in languages that are better represented in the training data. 
+ 
+
+
+Accuracy in Complex Reasoning:
+Despite being 
+trained on reasoning datasets, the model may occasionally produce 
+incorrect or incomplete answers for highly complex or multi-step 
+reasoning tasks.  
+
+
+Bias and Ethical Risks:
+Since the model is 
+trained on large datasets from diverse sources, it may exhibit biases 
+present in the training data, potentially leading to inappropriate or 
+biased outputs.  
+
+
+Dependency on Clear Instructions:
+The model’s
+ ability to generate accurate outputs relies heavily on the clarity and 
+specificity of user instructions. Ambiguous or vague instructions may 
+result in suboptimal responses.  
+
+
+Resource Requirements:
+As a large language 
+model with 10 billion parameters, it requires significant computational 
+resources for both training and inference, limiting its deployment in 
+low-resource environments.  
+
+
+Lack of Real-Time Understanding:
+LwQ-10B 
+lacks real-time understanding of current events or data beyond its 
+training, so it may not provide accurate responses for highly recent or 
+dynamic information.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)