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

Libraries

How to use OpceanAI/qwenti-inference with Transformers:

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

pipe = pipeline("text-generation", model="OpceanAI/qwenti-inference")
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("OpceanAI/qwenti-inference")
model = AutoModelForCausalLM.from_pretrained("OpceanAI/qwenti-inference", 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 OpceanAI/qwenti-inference with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OpceanAI/qwenti-inference

SGLang

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

Docker Model Runner
How to use OpceanAI/qwenti-inference with Docker Model Runner:
```
docker model run hf.co/OpceanAI/qwenti-inference
```

OpceanAI commited on 7 days ago

Commit

9eefd53

verified ·

1 Parent(s): 5b8649d

Model card: drop the logs entry

Browse files

Files changed (1) hide show

README.md +0 -1

README.md CHANGED Viewed

@@ -173,7 +173,6 @@ instruction.
 | `PAPER-tinyllama-rv32.md` | the paper source: method, verification, the 40/40 question, the merge, the limits |
 | `DESIGN-tinyllama.md` | the working design log with every wall and its fix |
 | `figures/` | instruction mix, quantization cost, 40-token agreement, speed, storage |
-| `logs/` | five circuit-vs-reference checkpoints, the G4 run, the 40-token run, a kernel dump |
 | `scripts/` | kernel (`tl.c`), assembly matvec (`vd2.S`), exporter, Python mirror, circuit checker, G4 driver, PDF builder |
 | `results.json` | raw result document of the G4 run |
 | `colab_bundle.zip` | the patched tree and scripts, ready for a Colab VM |

 | `PAPER-tinyllama-rv32.md` | the paper source: method, verification, the 40/40 question, the merge, the limits |
 | `DESIGN-tinyllama.md` | the working design log with every wall and its fix |
 | `figures/` | instruction mix, quantization cost, 40-token agreement, speed, storage |
 | `scripts/` | kernel (`tl.c`), assembly matvec (`vd2.S`), exporter, Python mirror, circuit checker, G4 driver, PDF builder |
 | `results.json` | raw result document of the G4 run |
 | `colab_bundle.zip` | the patched tree and scripts, ready for a Colab VM |