Instructions to use NotaMG/eqaq-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NotaMG/eqaq-v2 with Transformers:

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

pipe = pipeline("text-generation", model="NotaMG/eqaq-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NotaMG/eqaq-v2")
model = AutoModelForCausalLM.from_pretrained("NotaMG/eqaq-v2")
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 NotaMG/eqaq-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NotaMG/eqaq-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotaMG/eqaq-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NotaMG/eqaq-v2

SGLang

How to use NotaMG/eqaq-v2 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 "NotaMG/eqaq-v2" \
    --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": "NotaMG/eqaq-v2",
		"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 "NotaMG/eqaq-v2" \
        --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": "NotaMG/eqaq-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NotaMG/eqaq-v2 with Docker Model Runner:
```
docker model run hf.co/NotaMG/eqaq-v2
```

NotaMG commited on May 23

Commit

fd70217

verified ·

1 Parent(s): effeaeb

Correct speedup to local target-only baseline

Browse files

Files changed (1) hide show

README.md +26 -10

README.md CHANGED Viewed

@@ -51,18 +51,34 @@ Recommended route setup for the measured run:
 ### Local latency
-Measured with the EQC latency protocol: `/v1/completions`, logical batch size
-1, 5 warmup runs, 50 measurement runs per category.
-| Category | Prompt / new tokens | Median latency | Baseline | Speedup |
-|---|---:|---:|---:|---:|
-| short | 64 / 128 | 228.87 ms | 2582 ms | 11.28x |
-| medium | 2048 / 256 | 475.62 ms | 5441 ms | 11.44x |
-| long | 8192 / 256 | 847.43 ms | 6576 ms | 7.76x |
-Average local speedup was **9.41x** against the protocol baseline. A
-submission-aligned smoke run with a more conservative single-image setup
-measured about **4.39x** average speedup over 3 runs per category.
 Observed speculative accept rate in the active local SGLang run was low,
 roughly **6%** over recent decode batches, so the latency gain should be

 ### Local latency
+Measured with the EQC latency request shape: `/v1/completions`, logical batch
+size 1, 5 warmup runs, 50 measurement runs per category.
+The speedup below is computed against a target-only run measured on the same
+local machine, not against the fixed baseline constants embedded in the EQC
+protocol harness.
+| Category | Prompt / new tokens | Target-only median | EQAQ v2 median | Local speedup |
+|---|---:|---:|---:|---:|
+| short | 64 / 128 | 852.58 ms | 228.87 ms | 3.73x |
+| medium | 2048 / 256 | 1771.02 ms | 475.62 ms | 3.72x |
+| long | 8192 / 256 | 2179.81 ms | 847.43 ms | 2.57x |
+Average local speedup was **3.10x** using the average of category medians
+(`1601.14 ms / 517.31 ms`). The older **9.41x** figure comes from dividing by
+the EQC harness fixed baseline constants (`2582/5441/6576 ms`) and should not
+be interpreted as a speedup over a baseline measured on this machine.
+A submission-aligned smoke run with a more conservative single-image setup
+measured about **4.39x** against the same fixed protocol constants over 3 runs
+per category; it is included only as a packaging/protocol smoke result, not as
+the local target-only speedup.
+Baseline caveat: the target-only no-spec SGLang server crashed with the default
+piecewise CUDA graph path (`NoneType mrope_positions`), so the local
+target-only baseline was measured with `--disable-piecewise-cuda-graph` while
+keeping the same target model, endpoint, prompt/token protocol, CUDA graph
+batch sizes, and core SGLang serving options.
 Observed speculative accept rate in the active local SGLang run was low,
 roughly **6%** over recent decode batches, so the latency gain should be