stablelm-2-12b

163
120
12.0B
7 languages
—
by
stabilityai
Language Model
OTHER
12B params
New
163 downloads
Early-stage
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Mobile
Laptop
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27GB+ RAM
Mobile
Laptop
Server
Quick Summary

`Stable LM 2 12B` is a 12.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
12GB+ RAM

Training Data Analysis

🟡 Average (5.3/10)

Researched training datasets used by stablelm-2-12b with quality assessment

Specialized For

code
general
science
multilingual

Training Datasets (2)

the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
  • •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
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...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-2-12b",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.70,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

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