OpenCoder-1.5B-Base

157

23

1.5B

llama

by

infly

Language Model

OTHER

1.5B params

New

157 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

4GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ RAM

Code Examples

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

5. Inferencepythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             device_map="auto",
                                             trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=128)

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

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