Instructions to use tjarvis91/qovaryx-350m-scratch-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tjarvis91/qovaryx-350m-scratch-base with Transformers:

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

pipe = pipeline("text-generation", model="tjarvis91/qovaryx-350m-scratch-base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tjarvis91/qovaryx-350m-scratch-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tjarvis91/qovaryx-350m-scratch-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tjarvis91/qovaryx-350m-scratch-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tjarvis91/qovaryx-350m-scratch-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tjarvis91/qovaryx-350m-scratch-base

SGLang

How to use tjarvis91/qovaryx-350m-scratch-base 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 "tjarvis91/qovaryx-350m-scratch-base" \
    --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": "tjarvis91/qovaryx-350m-scratch-base",
		"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 "tjarvis91/qovaryx-350m-scratch-base" \
        --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": "tjarvis91/qovaryx-350m-scratch-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tjarvis91/qovaryx-350m-scratch-base with Docker Model Runner:
```
docker model run hf.co/tjarvis91/qovaryx-350m-scratch-base
```

Qovaryx 350M scratch base

Random-initialized weights. Training is required before this artifact can perform a useful language or classification task. The size in the repository name is a family label; it is not a benchmark result.

Status — October 3, 2026: Qovaryx is on the back burner. Desktop downloads are paused; there is no current public installer, purchase, or trial. These repositories remain as research references. Published weights are separate from desktop product availability.

Configuration

Field	Published value
Architecture	`QovaryxForCausalLM`
Width	1024
Decoder layers	24
Attention heads	16
Feed-forward width	2816
Vocabulary size	20,242
Configured maximum sequence length	2,048

These values come from config.json. They describe the supplied configuration, not measured training throughput, hardware compatibility, or model capability. Optional architecture mechanisms require compatible code, configuration, and training; their presence does not establish learned performance.

Repository format

This release contains pytorch_model.bin, a tokenizer, and custom Transformers wrapper files: _qovaryx_decoder.py, configuration_qovaryx.py, and modeling_qovaryx.py. train_quickstart.py is a training reference.

The recorded loading interface is:

from transformers import AutoConfig, AutoModelForCausalLM

repo = "tjarvis91/qovaryx-350m-scratch-base"
# Review the repository's custom Python code before enabling trust_remote_code.
config = AutoConfig.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)

This documents the bundled wrapper interface; it was not rerun during this documentation update. Choose the device, precision, dependencies, and training setup for your own environment. No specific GPU-memory fit or universal CPU support is certified here.

Evaluation and lineage

No task-performance result is claimed for these random weights. Results from separately trained specialist or Cluster Shell experiments cannot be attributed to this untrained checkpoint. Historical claims about frontier-level cognition, general hardware fit, or the quality of optional backends have been removed.

The trained Q-* specialist releases are separate checkpoints with their own configurations, tokenizers, dependencies, and evaluation limits. A scratch-base repository is not evidence that a particular specialist was trained directly from these exact uploaded weights.

License

Apache-2.0, as recorded in the existing model metadata. Review the supplied code and any third-party training data or dependencies separately.

Project and support

J.E. Herizon LLC, doing business as JE Horizon.

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