Amara-o2-7B-Qwen

2
4
7.0B
license:apache-2.0
by
Minami-su
Other
OTHER
7B params
New
2 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

This model is licensed under Apache 2.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
How to usepythontransformers
# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Minami-su/Amara-o2-7B-Qwen")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Amara-o2-7B-Qwen")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Amara-o2-7B-Qwen")

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