---
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.