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Model: prithivMLmods/Llama-3.1-5B-Instruct Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- llama3.1-5B
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- llama-3
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- Base_Ft
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- facebook
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- text-generation-inference
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- meta
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- ollama
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---
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# **Llama-3.1-5B-Instruct**
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Llama-3.1 is a collection of multilingual large language models (LLMs) that includes pretrained and instruction-tuned generative models in various sizes. The **Llama-3.1-5B-Instruct** model is part of the series optimized for multilingual dialogue use cases, offering powerful conversational abilities and outperforming many open-source and closed chat models on key industry benchmarks.
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## Model Overview
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- **Size**: 5B parameters
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- **Model Architecture**: Llama-3.1 is an auto-regressive language model using an optimized transformer architecture.
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- **Training**: The model is fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to align with human preferences, ensuring helpfulness, safety, and natural conversations.
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The **Llama-3.1-5B-Instruct** model is optimized for multilingual text generation and excels in a variety of dialog-based use cases. It is designed to handle a wide array of tasks, including question answering, translation, and instruction following.
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## How to Use
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### Requirements
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- Install the latest version of **Transformers**:
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```bash
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pip install --upgrade transformers
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```
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- Ensure you have **PyTorch** installed with support for `bfloat16`:
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```bash
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pip install torch
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```
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### Example Code
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Below is an example of how to use the **Llama-3.1-5B-Instruct** model for conversational inference:
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```python
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import transformers
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import torch
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# Define the model ID
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model_id = "prithivMLmods/Llama-3.1-5B-Instruct"
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# Set up the pipeline for text generation
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto", # Use the best device available
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)
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# Define conversation messages
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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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# Generate a response
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outputs = pipeline(
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messages,
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max_new_tokens=256,
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)
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# Print the generated response
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print(outputs[0]["generated_text"][-1])
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```
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### Model Details
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- **Model Type**: Instruction-Tuned Large Language Model (LLM)
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- **Training**: Trained using supervised fine-tuning and reinforcement learning with human feedback.
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- **Supported Tasks**: Dialogue generation, question answering, translation, and other text-based tasks.
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### Performance
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The **Llama-3.1-5B-Instruct** model outperforms many existing models on several benchmarks, making it a reliable choice for conversational AI tasks in multilingual environments.
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### Notes
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- This model is optimized for safety and helpfulness, ensuring a positive user experience.
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- The **torch_dtype** is set to `bfloat16` to optimize memory usage and performance.
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---
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config.json
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"LlamaForCausalLM"
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
{
|
||||
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}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:0968dcc0ee8e56c7dccd34a7f51f8065ea0cb9e2cc529e3243d1e5c0a4bdaa0c
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size 17208754
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2065
tokenizer_config.json
Normal file
2065
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user