MaziyarPanahi
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README.md
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---
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base_model: meta-llama/Meta-Llama-3-70B-Instruct
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library_name: transformers
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tags:
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- axolotl
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- finetune
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- dpo
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- facebook
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- meta
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- pytorch
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- llama
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- llama-3
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- chatml
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language:
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- en
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pipeline_tag: text-generation
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license: llama3
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license_name: llama3
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license_link: LICENSE
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inference: false
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model_creator: MaziyarPanahi
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model_name: Llama-3-70B-Instruct-DPO-v0.4
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quantized_by: MaziyarPanahi
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datasets:
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- Intel/orca_dpo_pairs
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---
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<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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# MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4
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This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-70B-Instruct` model.
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# Quantized GGUF
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All GGUF models are available here: [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4-GGUF)
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# Prompt Template
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This model uses `ChatML` prompt template:
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```
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<|im_start|>system
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{System}
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<|im_end|>
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<|im_start|>user
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{User}
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<|im_end|>
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<|im_start|>assistant
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{Assistant}
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````
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# How to use
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You can use this model by using `MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4` as the model name in Hugging Face's
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transformers library.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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from transformers import pipeline
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import torch
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model_id = "MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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# attn_implementation="flash_attention_2"
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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trust_remote_code=True
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)
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streamer = TextStreamer(tokenizer)
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pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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model_kwargs={"torch_dtype": torch.bfloat16},
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streamer=streamer
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)
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# Then you can use the pipeline to generate text.
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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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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|im_end|>"),
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tokenizer.convert_tokens_to_ids("<|eot_id|>") # safer to have this too
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]
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outputs = pipeline(
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prompt,
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max_new_tokens=2048,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.95,
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)
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print(outputs[0]["generated_text"][len(prompt):])
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```
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## Sample Response
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```
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<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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describe about pros and cons of docker system.<|im_end|>
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<|im_start|>assistant
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```
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```
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Here are the pros and cons of the Docker system:
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**Pros:**
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1. **Lightweight**: Docker containers are lightweight and use fewer resources compared to traditional virtual machines.
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2. **Isolation**: Docker provides a high level of isolation between containers, ensuring that each application runs independently without interfering with others.
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3. **Portability**: Docker containers are highly portable and can run on any system that supports Docker, without requiring specific dependencies or configurations.
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4. **Efficient Use of Resources**: Docker containers can share the same kernel and use fewer resources, making them more efficient than traditional virtual machines.
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5. **Fast Deployment**: Docker containers can be spun up quickly, making it ideal for rapid deployment and scaling of applications.
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6. **Easy Maintenance**: Docker provides a simple way to manage and update applications, with features like rolling updates and rollbacks.
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7. **Security**: Docker provides a secure environment for applications, with features like network isolation and encryption.
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8. **Large Ecosystem**: Docker has a large and active ecosystem, with a wide range of tools and plugins available for various tasks.
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9. **Version Control**: Docker provides an easy way to manage different versions of an application, with features like version tagging and rollbacks.
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10. **Cost-Effective**: Docker can help reduce costs by minimizing the need for virtual machines and reducing the overhead of resource allocation.
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**Cons:**
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1. **Steep Learning Curve**: Docker has a complex architecture and requires a good understanding of containerization, networking, and storage.
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2. **Overhead**: While Docker containers are lightweight, they still require some overhead in terms of resources and management.
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3. **Security Risks**: If not properly configured, Docker containers can introduce security risks, such as unauthorized access to the host system.
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4. **Limited Support for GUI Applications**: Docker is primarily designed for running command-line applications, and running GUI applications can be challenging.
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5. **Networking Complexity**: Docker's networking model can be complex, especially in large-scale deployments.
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6. **Storage Complexity**: Docker's storage model can be complex, especially when dealing with persistent data.
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7. **Limited Support for Legacy Applications**: Docker is designed for modern, cloud-native applications, and may not be suitable for legacy applications that rely on older technologies.
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8. **Dependence on Docker Hub**: Docker relies heavily on Docker Hub, which can be a single point of failure if the hub is down or experiencing issues.
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9. **Orchestration Complexity**: Docker provides a built-in orchestration tool, Docker Swarm, but it can be complex to set up and manage.
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10. **Vendor Lock-in**: Docker is a proprietary technology, and while it has a large ecosystem, it can be difficult to switch to alternative containerization platforms.
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Overall, Docker provides a powerful and flexible way to deploy and manage applications, but it requires careful planning, configuration, and management to ensure optimal performance and security.
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```
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