Instructions to use verify01234/try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use verify01234/try with Transformers:

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

pipe = pipeline("text-generation", model="verify01234/try")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("verify01234/try")
model = AutoModelForCausalLM.from_pretrained("verify01234/try", device_map="auto")
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 verify01234/try with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "verify01234/try"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "verify01234/try",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/verify01234/try

SGLang

How to use verify01234/try 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 "verify01234/try" \
    --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": "verify01234/try",
		"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 "verify01234/try" \
        --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": "verify01234/try",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use verify01234/try with Docker Model Runner:
```
docker model run hf.co/verify01234/try
```

Child_TextOnly_Fused (524.9M Parameters)

Model Summary

Child_TextOnly_Fused is a high-efficiency causal language model extracted and reconstructed from an enterprise AI-DNA (.aidna) genetic container. The model weights are serialized in native SafeTensors (model.safetensors) format with 100% loss-free weight preservation (exact 1:1 parameter fidelity).

This repository conforms strictly to standard Hugging Face directory specifications for auto-classes:

AutoModelForCausalLM.from_pretrained(...)
AutoTokenizer.from_pretrained(...)
Hugging Face pipeline("text-generation", ...)
Open LLM Leaderboard automated evaluation pipelines.

Architectural Specifications

Parameter	Specification Value
Model ID	`Child_TextOnly_Fused`
Origin Genotype ID	`Child_TextOnly_Fused`
Architecture Type	`LlamaForCausalLM`
Total Parameters	`524,907,008` (524.9M)
Hidden Dimension (`d_model`)	`576`
Number of Layers	`30`
Attention Heads (Q)	`9`
Key-Value Heads (KV)	`3` (Grouped Query Attention)
Intermediate Size (FFN)	`1536`
Vocabulary Size	`49,152`
Max Context Length	`8,192` tokens
Activation Function	`silu`
Weight Format	`model.safetensors` (bfloat16)
Serialization Standard	Hugging Face SafeTensors v0.8+

AI-DNA Lineage & Origin Container

Container Format: AI-DNA Enterprise Binary v2 Specification
Lineage Metadata: Fused from 5 parents: Parent_Text_SmolLM2, Parent_Text_Qwen2_5_0_5B, Parent_Text_SmolLM2_360M, Parent_Text_TinyLlama_1_1B, Parent_Text_OPT_125M
Tensor Integrity Check: Exact lossless reconstruction verified (SHA-256 payload verified).

Quickstart & Seamless Usage

Loading with Hugging Face Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "test_fused_folder"

# 1. Load Tokenizer seamlessly
tokenizer = AutoTokenizer.from_pretrained(model_path)

# 2. Load Model via AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16 if hasattr(torch, "bfloat16") else torch.float32,
    device_map="auto",
)

# 3. Generate with Chat Template
messages = [
    {"role": "user", "content": "Explain the concept of quantum computing in simple terms."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.7,
    top_p=0.95,
    do_sample=True,
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Direct Inference Pipeline

from transformers import pipeline

generator = pipeline("text-generation", model="test_fused_folder", device_map="auto")
results = generator("What are the key pillars of evolutionary computation?", max_new_tokens=64)
print(results[0]["generated_text"])

Open LLM Leaderboard Evaluation

This repository is formatted for submission to the Hugging Face Open LLM Leaderboard. All mandatory structural files (config.json, generation_config.json, model.safetensors, tokenizer.json, tokenizer_config.json, README.md) are present and verified.

Benchmark Suite	Metric	Focus Area
MMLU	5-shot Accuracy	Multidisciplinary Academic Knowledge
ARC (Challenge)	25-shot Accuracy	Complex Scientific Reasoning
GSM8K	5-shot CoT	Step-by-Step Mathematical Reasoning
HellaSwag	10-shot Accuracy	Commonsense Sentence Completion
TruthfulQA	0-shot MC2	Factuality & Hallucination Resistance
Winogrande	5-shot Accuracy	Coreference Pronoun Resolution

Verification & Integrity Checksums

Weights File: model.safetensors
Total Tensors: 559 tensors
Tensors by Dtype: {"bfloat16": 559}
Binary Size: 1001.18 MB

Converted using convert_aidna_to_safetensors.py — AI-DNA to SafeTensors Bridge.

Downloads last month: 367

Safetensors

Model size

0.1B params

Tensor type

BF16