seed-oss-tiny-random

155
36.0B
—
by
yujiepan
Language Model
OTHER
36B params
New
155 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
81GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
34GB+ RAM

Code Examples

pythontransformers
import os
import re

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yujiepan/seed-oss-tiny-random"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
messages = [
    {"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    thinking_budget=64  # control the thinking budget
)

outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=128)
output_text = tokenizer.decode(outputs[0])
print(output_text)
pythontransformers
import os
import re

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yujiepan/seed-oss-tiny-random"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
messages = [
    {"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    thinking_budget=64  # control the thinking budget
)

outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=128)
output_text = tokenizer.decode(outputs[0])
print(output_text)
pythontransformers
import os
import re

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yujiepan/seed-oss-tiny-random"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
messages = [
    {"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    thinking_budget=64  # control the thinking budget
)

outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=128)
output_text = tokenizer.decode(outputs[0])
print(output_text)

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