Instructions to use audiuphile/blazing-audio-slm-v7-4-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use audiuphile/blazing-audio-slm-v7-4-dev with Transformers:

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

pipe = pipeline("text-generation", model="audiuphile/blazing-audio-slm-v7-4-dev")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("audiuphile/blazing-audio-slm-v7-4-dev")
model = AutoModelForCausalLM.from_pretrained("audiuphile/blazing-audio-slm-v7-4-dev", 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 audiuphile/blazing-audio-slm-v7-4-dev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "audiuphile/blazing-audio-slm-v7-4-dev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "audiuphile/blazing-audio-slm-v7-4-dev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/audiuphile/blazing-audio-slm-v7-4-dev

SGLang

How to use audiuphile/blazing-audio-slm-v7-4-dev 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 "audiuphile/blazing-audio-slm-v7-4-dev" \
    --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": "audiuphile/blazing-audio-slm-v7-4-dev",
		"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 "audiuphile/blazing-audio-slm-v7-4-dev" \
        --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": "audiuphile/blazing-audio-slm-v7-4-dev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use audiuphile/blazing-audio-slm-v7-4-dev with Docker Model Runner:
```
docker model run hf.co/audiuphile/blazing-audio-slm-v7-4-dev
```

Blazing Audio SLM V7.4 — development checkpoint

Not a release candidate. V7.4 passed the aggregate numeric and tool-use gates but failed the minimum-family, general-adversarial, and measurement-deferral gates on the V1 development set. Manifest V2 remains sealed and has never been queried by this checkpoint.

This is a merged BF16 checkpoint of a 1.7B-parameter audio-system-design tutor. It is trained to choose one of four routes—calculate, defer_measurement, abstain, or explain. For numeric work, the model emits a canonical tool call; the separate verified Formula Core executes the call. The model explains, while Formula Core calculates.

The weights alone are not the deployed numeric system. Without the Formula Core executor they can emit tool-call text but cannot guarantee the reported numeric reliability.

Model and training identity

Base: Qwen/Qwen3-1.7B@70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Parent V7.2 adapter SHA-256: a534d27bcbf3b09ca7cb518e995b4852db27e79535ce9c4f29cfb6828fe22654
Training: one BF16 continuation epoch, learning rate 3e-5, batch 1, gradient accumulation 16, maximum sequence length 3,072, seed 74,007
Optimizer train/validation rows: 3,923 / 300
Train loss: 0.7039785876
Validation loss: 1.2190761566
Deployment-prompt SHA-256: a9e36d5c7bca98c986fea8909c9e53ff366d4c46810e220fab2910490a822970
Formula Core catalog SHA-256: 545e6a7696e40d7567aa82ad8a62b6d5c34272456e6df8ea5f46c6ef4170353f
Merged model.safetensors SHA-256: 518a646d9c0e9ff22db39c3c831e1757a5f73e2cff2b1600597cfe6467913b9d

V1 development evaluation

The tuned-only run evaluated 319 numeric rows and 68 behavioral rows. It was not a V2 confirmation and made no base- or frontier-superiority claim.

Gate	Result	Requirement	Status
Numeric headline	293/319 = 91.85%	>=85%	PASS
Numeric CI lower bound	88.71%	>=80%	PASS
Correct canonical tool	293/319 = 91.85%	>=90%	PASS
Arguments valid	313/319 = 98.12%	>=85%	PASS
Execution	309/319 = 96.87%	>=85%	PASS
Minimum strict family	16/24 = 66.67%	>=80%	FAIL
General adversarial	11/24 = 45.83%	>=80%	FAIL
Measurement deferral	5/12 = 41.67%	>=90%	FAIL
Calculable contrasts	29/32 = 90.63%	>=85%	PASS
False refusal	0%	<=10%	PASS
Unknown tools executed	0	0	PASS
Measurement tool calls	0	0	PASS

The weakest strict numeric families were measurements (16/24) and enclosures (26/36). The 26 numeric misses selected the wrong canonical tool and dropped, swapped, or invented inputs; they are not safe to repair in a compiler.

Intended use

Research and development of a tool-routed tutor for physical audio-system engineering.
Explanations of audio concepts where the explain route is appropriate.
Development testing with the matching V7.4 prompt, canonical catalog, schema validator, and Formula Core executor.

Do not represent this checkpoint as a production audio-design authority. Do not use it for safety-critical electrical work, and do not trust final crossover, response, impedance, polar, or sound-quality claims without physical measurement.

Limitations

V7.4 failed three substantive V1 release gates and is intentionally labeled as development-only.
The merged model does not bundle the Formula Core application layer.
It may confuse closely related tools and invent missing arguments.
It may explain a measurement boundary correctly while selecting the wrong first-line route.
V2 is sealed; there is no confirmation result.
No claim of superiority over Claude or another frontier model is made here.

License

The merged V7.4 checkpoint is published under the Apache License 2.0, consistent with the upstream Qwen/Qwen3-1.7B license. Preserve applicable upstream attribution and notices. The license does not convert this failed-gate development checkpoint into a production release or engineering certification.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for audiuphile/blazing-audio-slm-v7-4-dev

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Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

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