RedHatAI/Qwen3-Coder-Next-NVFP4

Name: RedHatAI/Qwen3-Coder-Next-NVFP4
Creator: RedHatAI
License: apache-2.0

text-generationVerified checksumsNVFP4quantizedllm-compressorcompressed-tensorsred hatsafetensors

License: apache-2.0
Size: 44 GB
Revision: main
Downloads in the last 30 days: 0 / 30 days

Mirrored from RedHatAI/Qwen3-Coder-Next-NVFP4

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Qwen3-Coder-Next-NVFP4

Model Overview

Model Architecture: Qwen3NextForCausalLM
- Input: Text
- Output: Text
Model Optimizations:
- Weight quantization: FP4
- Activation quantization: FP4
Release Date:
Version: 1.0
Model Developers:: Red Hat
ModelCar Storage URI: oci://registry.redhat.io/rhai/modelcar-qwen3-coder-next-nvfp4:3.0
Validated on vLLM: 0.14.1
Validated on RHAIIS: 3.4 EA1
Validated on RHOAI: 3.4 EA1

Quantized version of Qwen/Qwen3-Coder-Next.

Model Optimizations

This model was obtained by quantizing the weights and activations of Qwen/Qwen3-Coder-Next to FP4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights and activations of the linear operators within transformers blocks of the language model are quantized.

Deployment

Use with vLLM

Initialize vLLM server:

Send requests to the server:

python

# Your tool implementationdef square_the_number(num: float) -> dict:    return num ** 2
# Define Toolstools=[    {        "type":"function",        "function":{            "name": "square_the_number",            "description": "output the square of the number.",            "parameters": {                "type": "object",                "required": ["input_num"],                "properties": {                    'input_num': {                        'type': 'number',                         'description': 'input_num is a number that will be squared'                        }                },            }        }    }]
from openai import OpenAI# Define LLMclient = OpenAI(    # Use a custom endpoint compatible with OpenAI API    base_url='http://localhost:8000/v1',  # api_base    api_key="EMPTY") messages = [{'role': 'user', 'content': 'square the number 1024'}]
completion = client.chat.completions.create(    messages=messages,    model="RedHatAI/Qwen3-Coder-Next-NVFP4",    max_tokens=65536,    tools=tools,)
print(completion.choices[0])

Creation

This model was quantized using the llm-compressor library as shown below.

Creation details

python

from transformers import AutoModelForCausalLM, AutoTokenizerfrom datasets import load_dataset
from llmcompressor import oneshotfrom llmcompressor.modifiers.quantization import QuantizationModifierfrom compressed_tensors.offload import dispatch_model
MODEL_ID = "Qwen/Qwen3-Coder-Next"
# Load model.model = AutoModelForCausalLM.from_pretrained(    MODEL_ID,    torch_dtype="auto",    low_cpu_mem_usage=True,    trust_remote_code=True,)tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
DATASET_ID = "HuggingFaceH4/ultrachat_200k"DATASET_SPLIT = "train_sft"
# Select number of samplesNUM_CALIBRATION_SAMPLES = 20MAX_SEQUENCE_LENGTH = 2048
# Load dataset and preprocess.ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")ds = ds.shuffle(seed=42)

def preprocess(example):    return {        "text": tokenizer.apply_chat_template(            example["messages"],            tokenize=False,        )    }

ds = ds.map(preprocess)

# Tokenize inputs.def tokenize(sample):    return tokenizer(        sample["text"],        padding=False,        max_length=MAX_SEQUENCE_LENGTH,        truncation=True,        add_special_tokens=False,    )

ds = ds.map(tokenize, remove_columns=ds.column_names)


recipe = QuantizationModifier(    targets="Linear",    scheme="NVFP4",    weight_observer="mse",    ignore= ['re:.*lm_head', 're:.*mlp.gate$', 're:.*mlp.shared_expert_gate$', 're:.*linear_attn.*'],)

oneshot(    model=model,    dataset=ds,    recipe=recipe,    max_seq_length=MAX_SEQUENCE_LENGTH,    num_calibration_samples=NUM_CALIBRATION_SAMPLES,    moe_calibrate_all_experts=True,)

print("\n\n")print("========== SAMPLE GENERATION ==============")
dispatch_model(model)
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(    model.device)output = model.generate(input_ids, max_new_tokens=100)print(tokenizer.decode(output[0]))print("==========================================\n\n")

# Save to disk in compressed-tensors format.SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4"model.save_pretrained(SAVE_DIR, save_compressed=True)tokenizer.save_pretrained(SAVE_DIR)

Evaluation

The model was evaluated on the OpenLLM leaderboard task, using lm-evaluation-harness. vLLM was used for all evaluations.

Evaluation details

**Coding Benchmarks **

SWE-Bench

RedHatAI/Qwen3-Coder-Next-NVFP4

Qwen3-Coder-Next-NVFP4

Model Overview

Model Optimizations

Deployment

Use with vLLM

Creation

Evaluation

Accuracy

Qwen3-Coder-Next-NVFP4

Model Overview

Model Optimizations

Deployment

Use with vLLM

Creation

Evaluation

Accuracy