SJT-2B-V1.1

1
1
2.0B
—
by
Sakalti
Language Model
OTHER
2B params
New
1 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
5GB+ RAM
Mobile
Laptop
Server
Quick Summary

Base model: rinna/gemma-2-Baku-2b-it, prithivMLmods/GWQ2b.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Code Examples

samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))
samplepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
model = AutoModelForCausalLM.from_pretrained(
    "Sakalti/SJT-2B-V1.1",
    device_map="auto",
    torch_dtype=torch.float16,
)

input_text = "おはようこざいます！。"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0]))

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