Instructions to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with Transformers:

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

pipe = pipeline("text-generation", model="vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone

SGLang

How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone 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 "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone with Docker Model Runner:
```
docker model run hf.co/vtava/Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone
```

Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone

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---
library_name: transformers
pipeline_tag: text-generation
tags:
- tinycenn
- cenn
- language-modeling
- text-generation
- research
---

# Qwen3.5-0.8B-CeNN-Integrated-V1-Standalone

Research artifact from **TinyCeNN-LM**. Architecture: `TinyCeNN-LM experiment`.

## Architecture

- Architecture/run type: `TinyCeNN-LM experiment`
- Base model: `not recorded`
- Dataset: `Not recorded`
- Source code: https://github.com/vtavakkoli/TinyCeNN-LM

## Latest saved results

| Metric | Value |
|---|---:|
| `feature_dim` | 64 |

The Hugging Face repository keeps timestamped run artifacts under `runs/`. This preserves training reports, configs and run metadata independently of the temporary Colab filesystem.

## Saved experiment files

- `config.json`
- `generation_config.json`
- `release_report.json`
- `standalone_config.json`
- `tokenizer_config.json`

## Reproducibility

Run the matching notebook from the TinyCeNN-LM repository. Colab notebooks use a Hugging Face write token from the `HF_TOKEN` Colab Secret; tokens should never be pasted into notebook source.

## Limitations

This is a research checkpoint. Metrics saved here are the metrics produced by the corresponding training notebook/script; unless explicitly marked as held-out evaluation, they should not be treated as publication-grade benchmark results. Generation quality can differ substantially from the base model.

## Citation

If you use this experimental checkpoint, cite the TinyCeNN-LM repository and the upstream base model.