Instructions to use groxaxo/Hemmingway-1-AutoRound-W4G128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Hemmingway-1-AutoRound-W4G128 with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/Hemmingway-1-AutoRound-W4G128")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("groxaxo/Hemmingway-1-AutoRound-W4G128")
model = AutoModelForCausalLM.from_pretrained("groxaxo/Hemmingway-1-AutoRound-W4G128", 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 groxaxo/Hemmingway-1-AutoRound-W4G128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Hemmingway-1-AutoRound-W4G128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Hemmingway-1-AutoRound-W4G128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Hemmingway-1-AutoRound-W4G128

SGLang

How to use groxaxo/Hemmingway-1-AutoRound-W4G128 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 "groxaxo/Hemmingway-1-AutoRound-W4G128" \
    --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": "groxaxo/Hemmingway-1-AutoRound-W4G128",
		"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 "groxaxo/Hemmingway-1-AutoRound-W4G128" \
        --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": "groxaxo/Hemmingway-1-AutoRound-W4G128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Hemmingway-1-AutoRound-W4G128 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Hemmingway-1-AutoRound-W4G128
```

Hemmingway-1 — AutoRound W4G128

4-bit, group-size-128 AutoRound quantization of Altworld/Hemmingway-1, a 27B-parameter fine-tune of Qwen3.8-27B. This repository is an independent quantization, not the original model release. See the original model card for training, intended use, and benchmark information.

Quantization: AutoRound 0.15.1, bits=4, group_size=128, packing_format=auto_round:auto_awq, seqlen=1024, nsamples=64, iters=50. Certain layers remain in higher precision, as specified in quantization_config.json.

The included six-shard weight index and auxiliary model_extra_tensors.safetensors are needed together. Approximate download size: 18.65 GB (17.37 GiB). This is a quantization of an existing fine-tuned model, not a new fine-tuning run. Inference compatibility depends on a Transformers/AutoRound stack supporting this model architecture and quantization format; no inference benchmark or quality claim is made here.

Downloads last month: 186

Safetensors

Model size

6B params

Tensor type

I32

BF16

F16

Model tree for groxaxo/Hemmingway-1-AutoRound-W4G128

Base model

Qwen/Qwen3.8-27B

Finetuned

Altworld/Hemmingway-1

Quantized

(42)

this model