Instructions to use groxaxo/Qwento-Agentic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Qwento-Agentic with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/Qwento-Agentic")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("groxaxo/Qwento-Agentic")
model = AutoModelForMultimodalLM.from_pretrained("groxaxo/Qwento-Agentic", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use groxaxo/Qwento-Agentic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/groxaxo/Qwento-Agentic

SGLang

How to use groxaxo/Qwento-Agentic 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 "groxaxo/Qwento-Agentic" \
    --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": "groxaxo/Qwento-Agentic",
		"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 "groxaxo/Qwento-Agentic" \
        --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": "groxaxo/Qwento-Agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Qwento-Agentic with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwento-Agentic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwento-Agentic

Overview

Qwento-Agentic is a model checkpoint packaged for compatible Hugging Face runtimes, published by groxaxo. It is intended for open-source evaluation, reproducible experimentation, and compatible local or hosted inference workflows. The wording below is deliberately limited to what can be verified from this repository's metadata and artifacts.

At a glance

Field	Details
Format	Transformers
Source / base	`Qwen/Qwen-AgentWorld-35B-A3B`
Intended task	`image-text-to-text`
License	`apache-2.0`

What is included

*.safetensors (21 files)
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
Additional configuration, tokenizer, processor, or shard files (33 visible artifacts total)

Quick start

Getting started

Start with the upstream library named in the repository metadata and keep all configuration, tokenizer, processor, and weight files together. This repository is an artifact release, so the source project remains the authoritative reference for task-specific loading code.

Compatibility and responsible use

Use a runtime that explicitly supports this format, architecture, and modality.
Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
Review the source model card and license before redistribution or deployment.
Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.

Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.

⚠️ Test run. This is an early experimental checkpoint, not a finished model.

A QLoRA fine-tune merged into BF16, built on top of Qwen/Qwen-AgentWorld-35B-A3B (a Qwen3.5 MoE: 35B total / ~3B active, hybrid DeltaNet linear-attention + full-attention, 256 experts). It was trained on a curated set of publicly available datasets and is designed for coding tasks.

What this is

Type: test run — a single short curriculum stage (2K sequence length), early checkpoint.
Method: QLoRA (rank 16, α 32) applied to the model's sequence-mixing path (full-attention q/k/v/o + linear-attention input/output projections across all 40 layers), then merged into the BF16 base weights. The 256 MoE experts were left frozen.
Format: BF16 safetensors, drop-in with 🤗 Transformers / vLLM (same architecture and tokenizer as the base).

Training data (curated, publicly available)

A token-balanced blend of cleaned public datasets:

Source	Focus
`Jackrong/Claude-opus-4.7-TraceInversion-5000x`	reasoning / trace-inversion problem solving
`lordx64/reasoning-distill-claude-opus-4-7-max`	high-quality reasoning traces
`lordx64/reasoning-distill-opus-4-7-max-sft`	instruction-style reasoning SFT
`Infatoshi/kernelbench-mega-traces`	GPU-kernel coding traces
`Glint-Research/fable-5-traces`	multi-turn agentic coding (tool use)

All sources were structurally cleaned and quality-filtered before mixing.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained(
    "groxaxo/Qwento-Agentic", torch_dtype="bfloat16", device_map="auto", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("groxaxo/Qwento-Agentic")

Limitations

This is a preliminary test checkpoint from a short training run; it has not been benchmarked and should be treated as experimental. It inherits the license and any usage restrictions of the base model (Qwen/Qwen-AgentWorld-35B-A3B).

Downloads last month: 519

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for groxaxo/Qwento-Agentic

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen-AgentWorld-35B-A3B

Finetuned

(9)

this model

Quantizations

1 model