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---
license: llama3.1
language:
- en
base_model:
- prithivMLmods/Triangulum-10B
pipeline_tag: text-generation
library_name: transformers
tags:
- LlamaWithQuestions
- CoT
- Reasoner
- LWQ
---
# **LwQ-Reasoner-10B**
LwQ-Reasoner-10B (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-Reasoner-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-Reasoner-10B"
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])
```
# **Config and Base**
```json
{
"_name_or_path": "prithivMLmods/Triangulum-10B",
"architectures": [
"LlamaForCausalLM"
]
}
```
# **Intended Use**
1. **Multilingual Dialogue Systems**: LwQ-Reasoner-10B 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-Reasoner-10B 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-Reasoner-10B 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-Reasoner-10B 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 10 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. |