caselaw-cpt-8b

20
1
8.0B
2 languages
llama
by
yasserrmd
Language Model
OTHER
8B params
New
20 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Code Examples

🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🚀 Example Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("yasserrmd/caselaw-cpt-8b").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("yasserrmd/caselaw-cpt-8b")

prompt = "Q: What are the three conditions for res ipsa loquitur to apply?\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        top_p=0.9,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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