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"""Continue-1-OSS Model Implementation"""
from transformers.models.llama.modeling_llama import \
LlamaAttention as _BaseAttention
from transformers.models.llama.modeling_llama import \
LlamaDecoderLayer as _BaseDecoderLayer
from transformers.models.llama.modeling_llama import \
LlamaForCausalLM as _BaseModel
from transformers.models.llama.modeling_llama import LlamaMLP as _BaseMLP
from transformers.models.llama.modeling_llama import \
LlamaModel as _BaseTransformer
from transformers.models.llama.modeling_llama import \
LlamaRMSNorm as _BaseRMSNorm
from transformers.models.llama.modeling_llama import \
LlamaRotaryEmbedding as _BaseRotaryEmbedding
from .configuration_continue_oss import Continue1Config
# Continue-1-OSS Core Components
class Continue1RMSNorm(_BaseRMSNorm):
"""Continue-1-OSS Root Mean Square Layer Normalization"""
pass
class Continue1RotaryEmbedding(_BaseRotaryEmbedding):
"""Continue-1-OSS Rotary Position Embeddings"""
pass
class Continue1MLP(_BaseMLP):
"""Continue-1-OSS MLP (Feed-Forward Network)"""
pass
class Continue1Attention(_BaseAttention):
"""Continue-1-OSS Multi-Head Attention"""
pass
class Continue1DecoderLayer(_BaseDecoderLayer):
"""Continue-1-OSS Transformer Decoder Layer"""
pass
class Continue1Model(_BaseTransformer):
"""
Continue-1-OSS Transformer Model
Core transformer model without the language modeling head.
"""
config_class = Continue1Config
def __init__(self, config: Continue1Config):
super().__init__(config)
class Continue1ForCausalLM(_BaseModel):
"""
Continue-1-OSS Model for Causal Language Modeling
Designed by SVECTOR Corporation for high-quality text generation,
instruction following, and long-context understanding.
Example:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"SVECTOR-CORPORATION/Continue-1-OSS",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Continue-1-OSS")
messages = [{"role": "user", "content": "Hello There!"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100)
```
"""
config_class = Continue1Config
def __init__(self, config: Continue1Config):
super().__init__(config)
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