Instructions to use dominant-strategies/quai-igemm-qwen2.5-7b-w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dominant-strategies/quai-igemm-qwen2.5-7b-w8a8 with Transformers:

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

pipe = pipeline("text-generation", model="dominant-strategies/quai-igemm-qwen2.5-7b-w8a8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dominant-strategies/quai-igemm-qwen2.5-7b-w8a8")
model = AutoModelForCausalLM.from_pretrained("dominant-strategies/quai-igemm-qwen2.5-7b-w8a8", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dominant-strategies/quai-igemm-qwen2.5-7b-w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dominant-strategies/quai-igemm-qwen2.5-7b-w8a8

SGLang

How to use dominant-strategies/quai-igemm-qwen2.5-7b-w8a8 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 "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8" \
    --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": "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8",
		"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 "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8" \
        --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": "dominant-strategies/quai-igemm-qwen2.5-7b-w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dominant-strategies/quai-igemm-qwen2.5-7b-w8a8 with Docker Model Runner:
```
docker model run hf.co/dominant-strategies/quai-igemm-qwen2.5-7b-w8a8
```

Quai InferenceGemm Qwen2.5-7B W8A8

Built with Qwen.

This repository contains InferenceGemm/Tensor Work Proof W8A8 quantized checkpoint artifacts derived from Qwen/Qwen2.5-7B. It is not an official Qwen release.

igemm-quantized-*.safetensors: quantized tensor payloads.
manifest.json: verifier-facing tensor manifest and Merkle roots.
index.json: checkpoint index for the quantized tensor shards.
runtime-alias-audit.json, when present: runtime-to-manifest alias audit.
tokenizer/config files copied from the base model directory.

Benchmark Status

Quantized checkpoint prepared locally; not included in the paper benchmark table because no promoted strict-live accepted-receipt benchmark has been recorded for 7B yet.

The paper benchmark rows were produced with strict SGLang Tensor Work Receipt emission and Go verification. The raw benchmark logs, receipts, runtime attestation keys, and local evidence packet are intentionally not uploaded to this model repository.

Loading

These files are intended for the Quai InferenceGemm harness in this repository, not vanilla transformers weight loading:

checkpoints/<this-checkpoint>/

Use the base model tokenizer/config with the InferenceGemm quantized payloads and verifier manifest.

Upstream

Base model: Qwen/Qwen2.5-7B
Upstream license: apache-2.0

Redistribution must comply with the upstream model license and applicable export control restrictions.

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