Instructions to use SurgeFF/AriaV7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SurgeFF/AriaV7 with Transformers:

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

pipe = pipeline("text-generation", model="SurgeFF/AriaV7")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SurgeFF/AriaV7")
model = AutoModelForCausalLM.from_pretrained("SurgeFF/AriaV7", 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 SurgeFF/AriaV7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SurgeFF/AriaV7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SurgeFF/AriaV7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SurgeFF/AriaV7

SGLang

How to use SurgeFF/AriaV7 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 "SurgeFF/AriaV7" \
    --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": "SurgeFF/AriaV7",
		"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 "SurgeFF/AriaV7" \
        --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": "SurgeFF/AriaV7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SurgeFF/AriaV7 with Docker Model Runner:
```
docker model run hf.co/SurgeFF/AriaV7
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Aria V7.1

Aria is a 14B assistant built by Sergio Williams on Qwen3-14B-Base, developed as the core model for MnemonicAI. This repo's main holds V7.1, the current release. (V7.0 weights remain available in this repo's commit history.)

Benchmarks

100 held-out GSM8K-style math + 10 identity probes, identical raw plain-ChatML serving for all three (Intel Arc A770 / ollama):

Model	Math	Identity	Wall time
v5 (previous live)	46/100	0/10	695s
V7.0	69/100	8/10	837s
V7.1	71/100	8/10	514s

Honest read: +2 math over V7.0 is within noise on a 100-question set. The meaningful gains are the 38% latency reduction and a fully clean, verified training corpus. Against the previous live v5: +25 math points and 0→8 identity.

Training

Unsloth QLoRA on Qwen3-14B-Base, ~2.55 epochs, seq 2048, LoRA r32/α32
Target modules include lm_head + embed_tokens (see below), plain ChatML, response-only loss
17,522 verified examples, deduplicated: math 11,183 (answer-checked vs gold) · code 3,309 (execution-verified — every solution ran its own assert tests) · deep/ultra reasoning 1,206 · chat 937 · balancing 379 · multi-turn 250 · identity 205 · max-thinking 53
Teachers: kimi-k2.5/2.6/2.7-code, Claude Opus & Sonnet, Fable 5, deepseek-v4-pro
Dedup removed 501 duplicate prompts and 768 duplicate code solutions

The stop-token fix

Earlier versions rambled and never terminated. Two causes:

Serving with an injected empty <think>\n\n</think> block (enable_thinking=false) triggers non-stop generation.
More fundamentally: Qwen3-Base never emits <|im_end|> during pretraining, and attention/MLP-only LoRA can never surface that token — the output head must be trainable.

V7.1 fixes this structurally by putting lm_head+embed_tokens in the LoRA and leaving terminators unmasked in the loss. Result: a 5/5 clean-stop gate — every probe emits <|im_end|> and terminates.

Serving requirements (important)

Use the plain Qwen3 ChatML template. Never inject an empty <think> block / enable_thinking=false.
Stop tokens: <|im_end|> and <|endoftext|>.

<|im_start|>system
You are Aria, a helpful AI assistant created by Sergio Williams.<|im_end|>
<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant

Known quirks

A rare stray non-English token can appear immediately before the stop token (cosmetic; trim app-side).

Base model licence: Qwen3-14B-Base, Apache-2.0.

Downloads last month: 4

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for SurgeFF/AriaV7

Base model

Qwen/Qwen3-14B-Base

Finetuned

(80)

this model