88 lines
2.5 KiB
Markdown
88 lines
2.5 KiB
Markdown
---
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widget:
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- messages:
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- role: system
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content: >-
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You are a career counselor. The user will provide you with an individual
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looking for guidance in their professional life, and your task is to
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assist them in determining what careers they are most suited for based on
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their skills, interests, and experience. You should also conduct research
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into the various options available, explain the job market trends in
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different industries, and advice on which qualifications would be
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beneficial for pursuing particular fields.
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- role: user
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content: Hey friend!
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- role: assistant
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content: Hi! How may I help you?
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- role: user
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content: >-
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I am interested in developing a career in software engineering. What would
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you recommend me to do?
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- messages:
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- role: system
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content: You are a knowledgeable assistant. Help the user as much as you can.
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- role: user
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content: How to become smarter?
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- messages:
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- role: system
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content: You are a helpful assistant who provides concise responses.
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- role: user
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content: Hi!
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- role: assistant
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content: Hello there! How may I help you?
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- role: user
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content: >-
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I need to cook a simple dinner. What ingredients should I prepare for?
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- messages:
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- role: system
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content: >-
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You are a very creative assistant. User will give you a task, which you
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should complete with all your knowledge.
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- role: user
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content: >-
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Write the novel story of an RPG game about group of survivor
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post apocalyptic world.
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inference:
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parameters:
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max_new_tokens: 256
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temperature: 0.6
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top_p: 0.95
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top_k: 50
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repetition_penalty: 1.2
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base_model:
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- TinyLlama/TinyLlama-1.1B-Chat-v1.0
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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datasets:
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- Locutusque/Hercules-v3.0
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- Locutusque/hyperion-v2.0
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- argilla/OpenHermes2.5-dpo-binarized-alpha
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---
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "frankenmerger/MiniLlama-1.8b-Chat-v0.1"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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``` |