Qwen3-14B-quantized.w4a16

Name: Qwen3-14B-quantized.w4a16
Author: RedHatAI

460

license:apache-2.0

RedHatAI

Language Model

OTHER

14B params

New

460 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

32GB+ RAM

Mobile

Laptop

Server

Quick Summary

Model Overview - Model Architecture: Qwen3ForCausalLM - Input: Text - Output: Text - Model Optimizations: - Weight quantization: INT4 - Intended Use Cases: - Reasoning.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

14GB+ RAM

Code Examples

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Deploymentpythontransformers

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/Qwen3-14B-quantized.w4a16"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

messages = [
    {"role": "user", "content": prompt}
]

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

Creationpythontransformers

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
  
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]

num_samples = 1024
max_seq_len = 8192

model = AutoModelForCausalLM.from_pretrained(model_stub)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

def preprocess_fn(example):
    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
  
ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

# Configure the quantization algorithm and scheme
recipe = GPTQModifier(
    ignore=["lm_head"],
    sequential_targets=["Qwen3DecoderLayer"],
    targets="Linear",
    dampening_frac=0.01,
    scheme="W4A16",
  )

  # Apply quantization
  oneshot(
      model=model,
      dataset=ds, 
      recipe=recipe,
      max_seq_length=max_seq_len,
      num_calibration_samples=num_samples,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-quantized.w4a16"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

textvllm

lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/Qwen3-14B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
    --tasks openllm \
    --apply_chat_template\
    --fewshot_as_multiturn \
    --batch_size auto

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