ReflectionCoder-DS-33B
8.6K
4
33.0B
1 language
llama
by
SenseLLM
Language Model
OTHER
33B params
New
9K downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
74GB+ RAM
Mobile
Laptop
Server
Quick Summary
This model is licensed under the Apache 2.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
31GB+ RAM
Code Examples
pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)pythontransformers
import torch
from transformers import pipeline
chat = [
{"role": "user", "content": "<Your code instruction here>"}
]
generator = pipeline(
model="SenseLLM/ReflectionCoder-DS-33B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(chat, max_length=128, num_return_sequences=1)
print(result)Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
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year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
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primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
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primaryClass={cs.CL}
}Citationtext
@misc{ren2024reflectioncoder,
title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation},
author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li},
year={2024},
eprint={2405.17057},
archivePrefix={arXiv},
primaryClass={cs.CL}
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