From abe7a218c351a3d8825b03d304edc49756efa0d1 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 9 Aug 2026 07:39:13 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: RedHatAI/Qwen3-0.6B-quantized.w4a16 Source: Original Platform --- .gitattributes | 36 ++++ README.md | 461 ++++++++++++++++++++++++++++++++++++++++ added_tokens.json | 28 +++ config.json | 3 + configuration.json | 1 + generation_config.json | 13 ++ merges.txt | 3 + model.safetensors | 3 + recipe.yaml | 11 + special_tokens_map.json | 31 +++ tokenizer.json | 3 + tokenizer_config.json | 3 + vocab.json | 3 + 13 files changed, 599 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 added_tokens.json create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 merges.txt create mode 100644 model.safetensors create mode 100644 recipe.yaml create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 vocab.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..85880d8 --- /dev/null +++ b/README.md @@ -0,0 +1,461 @@ +--- +library_name: transformers +license: apache-2.0 +pipeline_tag: text-generation +base_model: +- Qwen/Qwen3-0.6B +tags: +- neuralmagic +- redhat +- llmcompressor +- quantized +- INT4 +--- + +# Qwen3-0.6B-quantized.w4a16 + +## Model Overview +- **Model Architecture:** Qwen3ForCausalLM + - **Input:** Text + - **Output:** Text +- **Model Optimizations:** + - **Weight quantization:** INT4 +- **Intended Use Cases:** + - Reasoning. + - Function calling. + - Subject matter experts via fine-tuning. + - Multilingual instruction following. + - Translation. +- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). +- **Release Date:** 05/05/2025 +- **Version:** 1.0 +- **Model Developers:** RedHat (Neural Magic) + +### Model Optimizations + +This model was obtained by quantizing the weights of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) to INT4 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 of the linear operators within transformers blocks are quantized. +Weights are quantized using a asymmetric per-group scheme, with group size 64. +The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. + + +## Deployment + +This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. + +```python +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer + +model_id = "RedHatAI/Qwen3-0.6B-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) +``` + +vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. + +## Creation + +
+ Creation details + This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. + + +```python +from llmcompressor.modifiers.quantization import GPTQModifier +from llmcompressor.transformers import oneshot +from transformers import AutoModelForCausalLM, AutoTokenizer + +# Load model +model_stub = "Qwen/Qwen3-0.6B" +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, + config_groups={ + "group0": { + "targets": ["Linear"] + "weights": { + "num_bits": 4, + "type": "int", + "strategy": "group", + "group_size": 64, + "symmetric": False, + "actorder": "weight", + "observer": "mse", + } + } + } + ) + + # 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}") + ``` +
+ + + +## Evaluation + +The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), and on reasoning tasks using [lighteval](https://github.com/neuralmagic/lighteval/tree/reasoning). +[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations. + +
+ Evaluation details + + **lm-evaluation-harness** + ``` + lm_eval \ + --model vllm \ + --model_args pretrained="RedHatAI/Qwen3-0.6B-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 + ``` + + ``` + lm_eval \ + --model vllm \ + --model_args pretrained="RedHatAI/Qwen3-0.6B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \ + --tasks mgsm \ + --apply_chat_template\ + --batch_size auto + ``` + + ``` + lm_eval \ + --model vllm \ + --model_args pretrained="RedHatAI/Qwen3-0.6B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=16384,enable_chunk_prefill=True,tensor_parallel_size=1 \ + --tasks leaderboard \ + --apply_chat_template\ + --fewshot_as_multiturn \ + --batch_size auto + ``` + + **lighteval** + + lighteval_model_arguments.yaml + ```yaml + model_parameters: + model_name: RedHatAI/Qwen3-0.6B-quantized.w4a16 + dtype: auto + gpu_memory_utilization: 0.9 + max_model_length: 40960 + generation_parameters: + temperature: 0.6 + top_k: 20 + min_p: 0.0 + top_p: 0.95 + max_new_tokens: 32768 + ``` + + ``` + lighteval vllm \ + --model_args lighteval_model_arguments.yaml \ + --tasks lighteval|aime24|0|0 \ + --use_chat_template = true + ``` + + ``` + lighteval vllm \ + --model_args lighteval_model_arguments.yaml \ + --tasks lighteval|aime25|0|0 \ + --use_chat_template = true + ``` + + ``` + lighteval vllm \ + --model_args lighteval_model_arguments.yaml \ + --tasks lighteval|math_500|0|0 \ + --use_chat_template = true + ``` + + ``` + lighteval vllm \ + --model_args lighteval_model_arguments.yaml \ + --tasks lighteval|gpqa:diamond|0|0 \ + --use_chat_template = true + ``` + + ``` + lighteval vllm \ + --model_args lighteval_model_arguments.yaml \ + --tasks extended|lcb:codegeneration \ + --use_chat_template = true + ``` + +
+ +### Accuracy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Category + Benchmark + Qwen3-0.6B + Qwen3-0.6B-quantized.w4a16
(this model) + 		Recovery +
OpenLLM v1 + MMLU (5-shot) + 42.82 + 39.80 + 93.00% +
ARC Challenge (25-shot) + 32.85 + 30.72 + 93.5% +
GSM-8K (5-shot, strict-match) + 1.82 + 2.20 + --- +
Hellaswag (10-shot) + 43.04 + 41.02 + 95.3% +
Winogrande (5-shot) + 54.54 + 54.62 + 100.1% +
TruthfulQA (0-shot, mc2) + 51.61 + 48.77 + 94.5% +
Average + 37.78 + 36.19 + 95.8% +
OpenLLM v2 + MMLU-Pro (5-shot) + 17.25 + 14.27 + --- +
IFEval (0-shot) + 62.83 + 55.81 + 88.8% +
BBH (3-shot) + 4.23 + 1.63 + --- +
Math-lvl-5 (4-shot) + 18.26 + 10.26 + --- +
GPQA (0-shot) + 0.00 + 0.00 + --- +
MuSR (0-shot) + 0.00 + 0.00 + --- +
Average + 17.10 + 13.66 + --- +
Multilingual + MGSM (0-shot) + 19.70 + 19.90 + --- +
Reasoning
(generation) + 		AIME 2024 + 9.69 + 3.44 + --- +
AIME 2025 + 13.13 + 6.98 + --- +
GPQA diamond + 29.29 + 27.78 + 94.8% +
Math-lvl-5 + 71.60 + 70.60 + 98.6% +
LiveCodeBench + 12.83 + 8.35 + --- +
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