---
license: llama3.2
datasets:
- AI-MO/NuminaMath-CoT
- prithivMLmods/Math-Solve
- amphora/QwQ-LongCoT-130K
- prithivMLmods/Deepthink-Reasoning
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Express
- Llama
- Ollama
- v.1
- text-generation-inference
---

# **Llama-Express.1**

Llama-Express.1 is a 1B model based on Llama 3.2 (1B), fine-tuned on long chain-of-thought 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"
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.