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meta-llama/Meta-Llama-3-70B-Instruct model.ChatML prompt template:<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4 as the model name in Hugging Face's
transformers library.1from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
2from transformers import pipeline
3import torch
4
5model_id = "MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.4"
6
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.bfloat16,
10 device_map="auto",
11 trust_remote_code=True,
12 # attn_implementation="flash_attention_2"
13)
14
15tokenizer = AutoTokenizer.from_pretrained(
16 model_id,
17 trust_remote_code=True
18)
19
20streamer = TextStreamer(tokenizer)
21
22pipeline = pipeline(
23 "text-generation",
24 model=model,
25 tokenizer=tokenizer,
26 model_kwargs={"torch_dtype": torch.bfloat16},
27 streamer=streamer
28)
29
30# Then you can use the pipeline to generate text.
31
32messages = [
33 {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
34 {"role": "user", "content": "Who are you?"},
35]
36
37prompt = tokenizer.apply_chat_template(
38 messages,
39 tokenize=False,
40 add_generation_prompt=True
41)
42
43terminators = [
44 tokenizer.eos_token_id,
45 tokenizer.convert_tokens_to_ids("<|im_end|>"),
46 tokenizer.convert_tokens_to_ids("<|eot_id|>") # safer to have this too
47]
48
49outputs = pipeline(
50 prompt,
51 max_new_tokens=2048,
52 eos_token_id=terminators,
53 do_sample=True,
54 temperature=0.6,
55 top_p=0.95,
56)
57print(outputs[0]["generated_text"][len(prompt):])<|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|>assistantHere 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.| Metric | Value |
|---|---|
| Avg. | 78.89 |
| AI2 Reasoning Challenge (25-Shot) | 72.61 |
| HellaSwag (10-Shot) | 86.03 |
| MMLU (5-Shot) | 80.50 |
| TruthfulQA (0-shot) | 63.26 |
| Winogrande (5-shot) | 83.58 |
| GSM8k (5-shot) | 87.34 |
| Metric | Value |
|---|---|
| Avg. | 78.89 |
| AI2 Reasoning Challenge (25-Shot) | 72.61 |
| HellaSwag (10-Shot) | 86.03 |
| MMLU (5-Shot) | 80.50 |
| TruthfulQA (0-shot) | 63.26 |
| Winogrande (5-shot) | 83.58 |
| GSM8k (5-shot) | 87.34 |