Create README.md

README.md

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