Instructions to use groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16 with Transformers:

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

pipe = pipeline("text-generation", model="groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16")
model = AutoModelForCausalLM.from_pretrained("groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16")
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/Qwen3-4B-Instruct-2507-heretic-W8A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16"
# 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/Qwen3-4B-Instruct-2507-heretic-W8A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16

SGLang

How to use groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16 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/Qwen3-4B-Instruct-2507-heretic-W8A16" \
    --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/Qwen3-4B-Instruct-2507-heretic-W8A16",
		"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/Qwen3-4B-Instruct-2507-heretic-W8A16" \
        --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/Qwen3-4B-Instruct-2507-heretic-W8A16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16
```

Qwen3-4B-Instruct-2507-heretic-W8A16

Overview

This is an 8-bit weight-only quantized version of Qwen3-4B-Instruct-2507-heretic using the W8A16 scheme, quantized with LLMCompressor.

Property	Value
Base Model	Qwen3-4B-Instruct-2507-heretic
Quantization	W8A16 (8-bit weights, 16-bit activations)
Quant Method	compressed-tensors (AWQ-compatible)
Model Size	~4.1 GB
Context Length	262,144 tokens

Quantization Details

Framework: LLMCompressor
Quantization Scheme: W8A16 (channel-wise symmetric int8)
Format: pack-quantized (compressed-tensors)
Target Layers: All Linear layers (except lm_head)

Performance

Perplexity (WikiText-2-raw-v1, test split)

Model	Perplexity	Degradation
Original FP16	17.4634	-
This Model (W8A16)	17.5556	+0.53%

Nearly lossless quantization with minimal perplexity degradation!

Usage

With vLLM

vllm serve groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16 \
  --quantization compressed-tensors \
  --trust-remote-code

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16"
)

messages = [{"role": "user", "content": "Hello, how are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Python API (vLLM)

from vllm import LLM, SamplingParams

llm = LLM(
    model="groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16",
    quantization="compressed-tensors",
    trust_remote_code=True
)

sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=256)
outputs = llm.generate(["Hello, how are you?"], sampling_params)
for output in outputs:
    print(output.outputs[0].text)

Model Architecture

Architecture: Qwen3ForCausalLM
Hidden Size: 2,560
Intermediate Size: 9,728
Attention Heads: 32
KV Heads: 8 (GQA)
Layers: 36
Vocab Size: 151,936
RoPE Theta: 5,000,000

Acknowledgements

Qwen Team for the original Qwen3 model
heretic-org for the fine-tuned variant
vLLM Team for LLMCompressor and vLLM

License

Apache 2.0 (inherited from base model)

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

I64

I32

BF16

Model tree for groxaxo/Qwen3-4B-Instruct-2507-heretic-W8A16

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

heretic-org/Qwen3-4B-Instruct-2507-heretic

Quantized

(7)

this model