RLPR-Gemma2-2B-it

Name: RLPR-Gemma2-2B-it
Author: openbmb

116

2.0B

1 language

license:apache-2.0

openbmb

Language Model

OTHER

2B params

New

116 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

5GB+ RAM

Mobile

Laptop

Server

Quick Summary

RLPR-Gemma2-2B-it is trained from Gemma2-2B-it with the RLPR framework, which eliminates reliance on external verifiers and is simple and generalizable for more domains.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by RLPR-Gemma2-2B-it with quality assessment

Specialized For

general

science

multilingual

reasoning

Training Datasets (3)

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...

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

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Usagepythontransformers

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Gemma2-2B-it")
model = AutoModelForCausalLM.from_pretrained(
    "openbmb/RLPR-Gemma2-2B-it",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

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