Instructions to use Manusagents/qyvos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Manusagents/qyvos with Transformers:

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

pipe = pipeline("text-generation", model="Manusagents/qyvos")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Manusagents/qyvos")
model = AutoModelForCausalLM.from_pretrained("Manusagents/qyvos")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Manusagents/qyvos with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Manusagents/qyvos

SGLang

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

Docker Model Runner
How to use Manusagents/qyvos with Docker Model Runner:
```
docker model run hf.co/Manusagents/qyvos
```

qyvos / MODEL_CARD.md

Jd Vijay

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Qyvos — Model Card

Model Description

Qyvos is a fine-tuned version of Qwen2.5-0.5B-Instruct, designed to behave as an autonomous AI agent following the ManusClaw architecture. It is fine-tuned via LoRA on Qwen2.5-0.5B-Instruct using a curated dataset of 42 examples covering identity reinforcement, coding, data analysis, DevOps, GitHub workflows, MLOps, deep research, agentic task decomposition, reasoning, and safety refusals.

Developer

The-JDdev, SHS Lab

Base Model

Qwen/Qwen2.5-0.5B-Instruct (Apache 2.0)

Training Details

Method: LoRA fine-tuning (rank=8, alpha=16, dropout=0.05)
Target modules: q_proj, v_proj
Optimizer: Adafactor
Learning rate: 3e-4, cosine schedule, 5% warmup
Epochs: 1
Batch size: 1 (effective batch size = 2 via gradient accumulation)
Max sequence length: 192 tokens
Precision: bfloat16
Hardware: CPU only (no GPU)
Training time: ~30 seconds for 21 optimizer steps
Trainable parameters: 540,672 (0.11% of total)

Training Data

42 hand-crafted examples in JSONL format with Qwen chat template:

Category	Count
identity	26
reasoning	5
coding	3
safety	2
research	1
data_analysis	1
mlops	1
devops	1
agentic	1
github	1

Examples were derived from ManusClaw's skill markdown files (app/skills/builtin/*.md), agent loop definitions (app/agent/manus.py), and identity guard logic (app/agent/identity_guard.py).

Intended Use

Autonomous AI agent identity for the ManusClaw framework
Educational reference for LoRA fine-tuning on small models
Local inference on consumer hardware (CPU-only capable)
Building block for larger Qyvos variants

Out-of-Scope Use

High-stakes decision making (medical, legal, financial)
Production deployment without further evaluation
Tasks requiring benchmark-grade coding/reasoning accuracy

Evaluation

Manual inference tests confirmed:

✅ Correctly identifies as "Qyvos" (not GPT/Claude/Gemini/LLaMA)
✅ Resists simple jailbreak attempts
✅ Produces correct Python code for simple tasks (palindrome check, merge sorted lists)
✅ Solves basic math word problems with step-by-step reasoning
⚠️ Some jailbreak resistance is partial (0.5B model limitation)

Formal benchmarks (HumanEval, GSM8K, AgentBench) have not been run due to compute constraints.

Limitations

Small base model: Qwen2.5-0.5B has limited capacity. For production, use larger bases.
Limited training data: 42 examples is a starting point; iterative refinement needed.
CPU-only training: Single epoch with short sequences limits quality.
No formal benchmarking: Performance numbers vs. base model not measured.
Jailbreak resistance is best-effort: Small models can be coerced with persistence.

Ethical Considerations

Qyvos is trained to refuse unethical requests (malware, unauthorized access).
Identity protocol prevents the model from impersonating other AI systems.
The model does not store user data or make external calls.

Manusagents
/

qyvos