Llama-3.2-3B-Instruct-lyrics

Name: Llama-3.2-3B-Instruct-lyrics
Author: aifeifei798

11 languages

llama

aifeifei798

Language Model

OTHER

3B params

New

1 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

7GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

3GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by Llama-3.2-3B-Instruct-lyrics with quality assessment

Specialized For

general

science

multilingual

reasoning

Training Datasets (4)

common crawl

🔴 2.5/10

general

science

Key Strengths

•Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
•Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
•Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...

Considerations

•Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
•Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

🔵 6/10

general

multilingual

Key Strengths

•Scale and Accessibility: 750GB of publicly available, filtered text
•Systematic Filtering: Documented heuristics enable reproducibility
•Language Diversity: Despite English-only, captures diverse writing styles

Considerations

•English-Only: Limits multilingual applications
•Filtering Limitations: Offensive content and low-quality text remain despite filtering

wikipedia

🟡 5/10

science

multilingual

Key Strengths

•High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
•Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
•Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...

Considerations

•Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
•Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

arxiv

🟡 5.5/10

science

reasoning

Key Strengths

•Scientific Authority: Peer-reviewed content from established repository
•Domain-Specific: Specialized vocabulary and concepts
•Mathematical Content: Includes complex equations and notation

Considerations

•Specialized: Primarily technical and mathematical content
•English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)

Program:pythontransformers

from unsloth import FastLanguageModel
from unsloth import PatchFastRL
PatchFastRL("GRPO", FastLanguageModel)
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct"
    max_seq_length = max_seq_length,
    dtype = dtype,
    # load_in_4bit = load_in_4bit,
    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "", # instruction
        """ new write a pop love song. 
        song name:
        Style of Music(five components: genre, instrument, mood, gender, and timbre. ):
        [Intro]
        [Verse 1]
        [Chorus]
        [Verse 2]
        [Chorus]
        [Bridge]
        [Chorus]
        [Outro] """, # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1024)