Update README.md

README.md

@@ -18,6 +18,80 @@ 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)