77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
---
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license: llama3.2
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- thinker
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- llama
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- express
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---
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# **Llama-Express.1-Tiny**
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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.
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# **Use with transformers**
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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.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import torch
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from transformers import pipeline
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model_id = "prithivMLmods/Llama-Express.1-Tiny"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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# **Intended Use**
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1. **Multilingual Dialogue**:
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- Designed for high-quality, multilingual conversations, making it suitable for applications requiring natural, fluid dialogue across languages.
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2. **Agentic Retrieval**:
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- Optimized for retrieval-based tasks where reasoning and contextual chaining are crucial for extracting and summarizing relevant information.
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3. **Summarization Tasks**:
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- Effective in generating concise and accurate summaries from complex and lengthy texts, suitable for academic, professional, and casual use cases.
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4. **Instruction-Following Applications**:
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- Fine-tuned for tasks requiring adherence to user-provided instructions, making it ideal for automation workflows, content creation, and virtual assistant integrations.
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# **Limitations**
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1. **Monomodal Focus**:
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- As a text-only model, it cannot process multimodal inputs like images, audio, or videos, limiting its versatility in multimedia applications.
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2. **Context Length Constraints**:
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- While optimized for long chain-of-thought reasoning, extreme cases with very large contexts may still lead to degraded performance or truncation issues.
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3. **Bias and Ethics**:
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- The model might reflect biases present in the training datasets, potentially resulting in outputs that could be culturally insensitive or inappropriate.
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4. **Performance in Low-Resource Languages**:
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- While multilingual, its effectiveness may vary across languages, with possible performance drops in underrepresented or low-resource languages.
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5. **Dependency on Input Quality**:
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- 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.
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6. **Lack of Real-Time Internet Access**:
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- Without real-time retrieval capabilities, it cannot provide up-to-date information or verify facts against the latest data.
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