Instructions to use chris320211/smoothquant-phi35-mini-vastrtx3090 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chris320211/smoothquant-phi35-mini-vastrtx3090 with Transformers:

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

pipe = pipeline("text-generation", model="chris320211/smoothquant-phi35-mini-vastrtx3090")
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("chris320211/smoothquant-phi35-mini-vastrtx3090")
model = AutoModelForCausalLM.from_pretrained("chris320211/smoothquant-phi35-mini-vastrtx3090", 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 chris320211/smoothquant-phi35-mini-vastrtx3090 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chris320211/smoothquant-phi35-mini-vastrtx3090"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chris320211/smoothquant-phi35-mini-vastrtx3090",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chris320211/smoothquant-phi35-mini-vastrtx3090

SGLang

How to use chris320211/smoothquant-phi35-mini-vastrtx3090 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 "chris320211/smoothquant-phi35-mini-vastrtx3090" \
    --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": "chris320211/smoothquant-phi35-mini-vastrtx3090",
		"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 "chris320211/smoothquant-phi35-mini-vastrtx3090" \
        --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": "chris320211/smoothquant-phi35-mini-vastrtx3090",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chris320211/smoothquant-phi35-mini-vastrtx3090 with Docker Model Runner:
```
docker model run hf.co/chris320211/smoothquant-phi35-mini-vastrtx3090
```

SmoothQuant microsoft/Phi-3.5-mini-instruct

Quantized derivative of microsoft/Phi-3.5-mini-instruct, made with QuantPort. Not Hub fp16. Weights are the packed/runtime artifact from job 20261102T212454Z-0531e9.

WikiText-2 (test, 2048-token windows, 65504 tokens)

Measured on NVIDIA GeForce RTX 3090 (vast-rtx3090) against the original fp16 snapshot. Numbers copied from jobs/20261102T212454Z-0531e9/benchmark.json.

	Quantized	fp16 snapshot
Perplexity	6.822674	6.625631
NLL loss	1.920252	1.890946
Tokens/s (2048 prefill, after warmup)	12268.5	4218.8
Peak VRAM (GB)	5.908	9.681

ppl_ratio: 1.029740 (quality_ok=True)
improved_throughput: True
improved_vram: True

Pull

from huggingface_hub import snapshot_download
path = snapshot_download("chris320211/smoothquant-phi35-mini-vastrtx3090")

This is not a drop-in AutoModelForCausalLM.from_pretrained checkpoint. Reload with the included quant_agent_inference_adapter.py after the method repo (and overlay, if any) is on QUANT_AGENT_METHOD_REPO. See quantization_config.json.

Serve with vLLM

A native vLLM build of these weights: chris320211/smoothquant-phi35-mini-vastrtx3090-vllm.

vllm serve chris320211/smoothquant-phi35-mini-vastrtx3090-vllm

vLLM PPL ratio 1.0290 against vLLM fp16; prefill 2.05×, decode 1.62× (batch 1), 1.49× (batch 16). Measured on NVIDIA GeForce RTX 3090 with vLLM 0.30.0.

License

Inherits the license of microsoft/Phi-3.5-mini-instruct (mit), plus the method repository license. Keep LICENSE / NOTICE.md from the snapshot when present.

Downloads last month: 361

Safetensors

Model size

4B params

Tensor type

F16

Model tree for chris320211/smoothquant-phi35-mini-vastrtx3090

Base model

microsoft/Phi-3.5-mini-instruct

Quantized

(204)

this model

Collection including chris320211/smoothquant-phi35-mini-vastrtx3090

QuantPort library

Collection

Quantized LLMs made and measured with QuantPort (github.com/chris320211/QuantPort). Rankings live in its library/LIBRARY.md. • 11 items • Updated 3 days ago