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license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- express
- math
- llama3.2
---
# **Llama-Express.1-Math**
Llama-Express.1-Math is a 1B model based on Llama 3.2 (1B), fine-tuned on long chain-of-thought math datasets. This instruction-tuned, text-only model is optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. It outperforms many of the available open-source and closed chat models.
# **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 torch
from transformers import pipeline
model_id = "prithivMLmods/Llama-Express.1-Math"
pipe = pipeline(
"text-generation",
model=model_id,
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 = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
# **Intended Use**
1. **Multilingual Dialogue**:
- Designed for high-quality, multilingual conversations, making it suitable for applications requiring natural, fluid dialogue across languages.
2. **Agentic Retrieval**:
- Optimized for retrieval-based tasks where reasoning and contextual chaining are crucial for extracting and summarizing relevant information.
3. **Summarization Tasks**:
- Effective in generating concise and accurate summaries from complex and lengthy texts, suitable for academic, professional, and casual use cases.
4. **Instruction-Following Applications**:
- Fine-tuned for tasks requiring adherence to user-provided instructions, making it ideal for automation workflows, content creation, and virtual assistant integrations.
# **Limitations**
1. **Monomodal Focus**:
- As a text-only model, it cannot process multimodal inputs like images, audio, or videos, limiting its versatility in multimedia applications.
2. **Context Length Constraints**:
- While optimized for long chain-of-thought reasoning, extreme cases with very large contexts may still lead to degraded performance or truncation issues.
3. **Bias and Ethics**:
- The model might reflect biases present in the training datasets, potentially resulting in outputs that could be culturally insensitive or inappropriate.
4. **Performance in Low-Resource Languages**:
- While multilingual, its effectiveness may vary across languages, with possible performance drops in underrepresented or low-resource languages.
5. **Dependency on Input Quality**:
- The model's output is heavily influenced by the clarity and specificity of the input instructions. Ambiguous or vague prompts may lead to suboptimal results.
6. **Lack of Real-Time Internet Access**:
- Without real-time retrieval capabilities, it cannot provide up-to-date information or verify facts against the latest data.
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