Instructions to use Conlanger-LLM-CLEM/Tiny-Nelya-neko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Conlanger-LLM-CLEM/Tiny-Nelya-neko with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Conlanger-LLM-CLEM/Tiny-Nelya-neko")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Conlanger-LLM-CLEM/Tiny-Nelya-neko")
model = AutoModelForCausalLM.from_pretrained("Conlanger-LLM-CLEM/Tiny-Nelya-neko")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Conlanger-LLM-CLEM/Tiny-Nelya-neko with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Conlanger-LLM-CLEM/Tiny-Nelya-neko"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Conlanger-LLM-CLEM/Tiny-Nelya-neko",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Conlanger-LLM-CLEM/Tiny-Nelya-neko

SGLang

How to use Conlanger-LLM-CLEM/Tiny-Nelya-neko with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Conlanger-LLM-CLEM/Tiny-Nelya-neko" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Conlanger-LLM-CLEM/Tiny-Nelya-neko",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Conlanger-LLM-CLEM/Tiny-Nelya-neko" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Conlanger-LLM-CLEM/Tiny-Nelya-neko",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Conlanger-LLM-CLEM/Tiny-Nelya-neko with Docker Model Runner:
```
docker model run hf.co/Conlanger-LLM-CLEM/Tiny-Nelya-neko
```

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📄 Model Card: Nelya-neko

🌟 Model Overview Nelya-neko is a Small Language Model (SLM) with 124 million parameters, pre-trained on the Nekolien constructed language (intellectual property of LLm-Clem). It is the first model in the new generation of Clemylia architectures designed for advanced research tasks in conlangs (constructed languages) and long-context processing.

🛠️ Technical Details and Architecture

Feature	Value	Impact Note
Family / Type	Foundation Model (Base) / SLM	Requires fine-tuning for alignment and downstream applications.
Developer	Clemylia (LLm-Clem)	Created from scratch (architecture, tokenizer, pre-training).
Parameters	124 Million	Size optimized for efficiency and deployment on consumer-grade hardware.
Context Window	7000 Tokens	Major Innovation: Enables processing of full documents and long-form Nekolien conversations.
Language	Nekolien (Constructed Language)	Ultra-specialized. Should not be used for natural languages without extensive fine-tuning.
Tokenizer	Nekolien-tokenizer	Proprietary tokenizer built from scratch, essential for decoding and encoding Nekolien.

🔑 Special Tokens (Included in Nekolien-tokenizer)

The model uses a set of special tokens to structure data and enable future alignment tasks:

Token	Conventional Role	Specific Function
	UNK (Unknown)	Handles unknown sequences not present in the Nekolien corpus.
	CLS (Classifier)	Classification token for sequence encapsulation (useful for fine-tuning).
	SEP (Separator)	Used to mark the boundary between different parts of a text sequence.
	MASK	Required for Masked Language Modeling (MLM) and prediction tasks in fine-tuning.
	Memory / Metadata	Unique token, potentially related to the efficient management of the extended context (7000 tokens).
	Padding	Ensures sequence length consistency for GPU efficiency.

📜 License and Usage Restrictions

License: LRUNDL (Limited Distinction Research Non-Commercial License)

Attribution: All derivatives (fine-tuned models) must clearly attribute authorship to LLm-Clem.
Restriction: Use of Nelya-neko is strictly limited to research and non-commercial experimentation.
Compliance: Derivative works must adhere to the LRUNDL (no more permissive licenses, such as MIT, can be applied).

💡 Intended Use and Limitations

Intended Use

Conlang Research: Studying language modeling on constructed linguistic systems.
Nekolien Dataset Creation: Generating coherent corpora for fine-tuning.
Base for Specialized Assistants: Developing bots for the Nekolien language following alignment fine-tuning. Limitations and Precautions
Not Aligned: As a pure foundation model, Nelya-neko produces thematic text continuation, not structured responses (requires fine-tuning for instruction following).
Monolingual: Performance in any language other than Nekolien is nil or not guaranteed.
Access: The model and its tokenizer are subject to access restrictions managed by LLm-Clem.

🚀 Next Steps for Deployment

To transition from this foundation model to a functional application, Alignment Fine-Tuning (based on Nekolien instruction/response pairs) is necessary to instill the desired obedience and persona. Nekolien variant of this model: Original/Central Nekolien. Nelya-neko complies with the rules of the Nekolien Academy: https://neko-lexicon-archives.lovable.app/

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Safetensors

Model size

0.1B params

Tensor type

F32

Collection including Conlanger-LLM-CLEM/Tiny-Nelya-neko

Nelya-neko LLM

Collection

8 items • Updated Mar 24