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Model: prithivMLmods/LwQ-10B-Instruct
Source: Original Platform

README.md

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+---
+license: llama3.1
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- LwQ
+- safetensors
+- Llama3.1
+---
+ 
+![10b.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/qd7Gw46jaK48VGjLsk5Qg.gif)
+
+# **LwQ-10B-Instruct**
+
+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`.
+
+```python
+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**
+
+1. **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.  
+
+2. **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.  
+
+3. **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.  
+
+4. **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.  
+
+5. **Content Creation and Summarization**:  
+   LwQ-10B can generate high-quality content, including articles, reports, and summaries, across different languages and domains.  
+
+# **Limitations**
+
+1. **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.  
+
+2. **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.  
+
+3. **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.  
+
+4. **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.  
+
+5. **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.  
+
+6. **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.  
+
+7. **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.