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Model: prithivMLmods/Llama-Express.1-Tiny
Source: Original Platform

README.md

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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:
+- thinker
+- llama
+- express
+---
+# **Llama-Express.1-Tiny**
+
+Llama-Express.1-Tiny is a 1B model based on Llama 3.2 (1B), fine-tuned on long chain-of-thought thinker 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-Tiny"
+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.