Instructions to use deqing/llama-window-2-old with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deqing/llama-window-2-old with Transformers:

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

pipe = pipeline("text-generation", model="deqing/llama-window-2-old")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deqing/llama-window-2-old")
model = AutoModelForCausalLM.from_pretrained("deqing/llama-window-2-old")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deqing/llama-window-2-old with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deqing/llama-window-2-old"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deqing/llama-window-2-old",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deqing/llama-window-2-old

SGLang

How to use deqing/llama-window-2-old 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 "deqing/llama-window-2-old" \
    --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": "deqing/llama-window-2-old",
		"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 "deqing/llama-window-2-old" \
        --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": "deqing/llama-window-2-old",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deqing/llama-window-2-old with Docker Model Runner:
```
docker model run hf.co/deqing/llama-window-2-old
```

llama-window-2-old / README.md

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	---
	library_name: transformers
	tags:
	- convergent-evolution
	- fourier-features
	- number-embeddings
	license: mit
	datasets:
	- HuggingFaceFW/fineweb-edu
	---

	# convergent-llama-300M-muon-window_2

	A 300M-parameter language model trained from scratch on FineWeb-Edu 10BT (~9.4B tokens, 1 epoch) as part of the Convergent Evolution project, which investigates how Fourier features emerge in LLM number embeddings.

	## Model details

	\| \| \|
	\|---\|---\|
	\| Architecture \| LLaMA-style Transformer (12 layers, 1024 hidden, 16 heads, GQA) \|
	\| Parameters \| ~300M \|
	\| Optimizer \| Muon (for 2D weights) + AdamW (for embeddings/bias/norm) \|
	\| Data perturbation \| window-2 context (bigram-level context only) \|
	\| Training data \| [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) sample-10BT (~9.4B tokens) \|
	\| Context length \| 1024 \|
	\| Tokenizer \| Llama 3 (128K vocab) \|
	\| Batch size \| 512 sequences \|

	## Training dynamics

	Intermediate checkpoints are saved as branches: `tokens-200M`, `tokens-400M`, ..., `tokens-9.6B`.

	```python
	from transformers import AutoModelForCausalLM

	# Load final checkpoint
	model = AutoModelForCausalLM.from_pretrained("deqing/convergent-llama-300M-muon-window_2")

	# Load intermediate checkpoint (e.g., at 1B tokens)
	model = AutoModelForCausalLM.from_pretrained("deqing/convergent-llama-300M-muon-window_2", revision="tokens-1B")
	```

	## Citation

	Paper forthcoming.