---
license: llama3.1
language:
- en
pipeline_tag: text-generation
tags:
- text-generation-inference
base_model:
- meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
---

![30B.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Yd7ju9VUxX91yc4MkNTSa.gif)

# **LwQ-30B-Instruct**

LwQ-30B-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 utilizing 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-30B 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-30B-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 Dialogue Systems**: LwQ-30B-Instruct is designed for creating conversational agents capable of engaging in dialogues across multiple languages, making it suitable for global customer support and multilingual chatbots.  
   
2. **Instruction-Following Tasks**: The model excels at tasks requiring adherence to specific instructions or keywords embedded in the input, such as form completion, task automation, and guided workflows.  
   
3. **Mathematical Reasoning**: With specialized training on synthetic reasoning datasets, LwQ-30B can perform complex mathematical reasoning and problem-solving, making it useful for educational platforms, tutoring systems, and research assistance.  

4. **Context-Based Problem Solving**: The model is optimized to handle contextually rich problems, allowing it to generate context-aware responses for applications such as summarization, question answering, and decision support.  

5. **Content Generation**: It can generate high-quality content, including articles, reports, summaries, and creative writing, across various domains and languages.  

6. **Knowledge Retrieval**: LwQ-30B can retrieve and synthesize information from its trained data to answer factual questions, assist in research, and support knowledge-intensive tasks.  

# **Limitations**  

1. **Performance Variability Across Languages**: While the model supports multiple languages, its performance may vary depending on the language, with better results for languages more prevalent in its training data.  

2. **Handling of Niche Topics**: The model may struggle to provide accurate information or generate high-quality content for highly specialized or niche topics not covered extensively in its training data.  

3. **Complex Multi-Step Reasoning**: Although trained on reasoning datasets, the model may still occasionally produce incorrect or incomplete results for multi-step or highly complex reasoning tasks.  

4. **Bias and Ethical Concerns**: Since LwQ-30B is trained on large, publicly available datasets, it may inherit biases present in the data, leading to potential ethical concerns or inappropriate outputs in certain contexts.  

5. **Context Limitations**: The model has a finite context window, which may lead to incomplete understanding or response generation for tasks requiring extensive context or very long input texts.  

6. **Resource Intensive**: As a large-scale model with 30 billion parameters, it requires substantial computational resources for both inference and deployment, limiting its use in resource-constrained environments.  

7. **Instruction Ambiguity**: The model’s performance can degrade when instructions are ambiguous, vague, or conflicting, potentially leading to outputs that do not align with user expectations.