xgen-small-9B-base-r

2
1 language
llama
by
Salesforce
Language Model
OTHER
9B params
New
0 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM

Code Examples

Usagepythontransformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Salesforce/xgen-small-9B-base-r"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto"
).to(device)

prompt = "What is Salesforce?"
inputs = tokenizer(
    prompt,
    return_tensors="pt",
    padding=False,
    truncation=True
).to(device)

generated = model.generate(**inputs, max_new_tokens=32)
output = tokenizer.decode(
    generated[0],
    skip_special_tokens=True,
)
print(output)
Usagepythontransformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Salesforce/xgen-small-9B-base-r"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto"
).to(device)

prompt = "What is Salesforce?"
inputs = tokenizer(
    prompt,
    return_tensors="pt",
    padding=False,
    truncation=True
).to(device)

generated = model.generate(**inputs, max_new_tokens=32)
output = tokenizer.decode(
    generated[0],
    skip_special_tokens=True,
)
print(output)
Usagepythontransformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Salesforce/xgen-small-9B-base-r"
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto"
).to(device)

prompt = "What is Salesforce?"
inputs = tokenizer(
    prompt,
    return_tensors="pt",
    padding=False,
    truncation=True
).to(device)

generated = model.generate(**inputs, max_new_tokens=32)
output = tokenizer.decode(
    generated[0],
    skip_special_tokens=True,
)
print(output)

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