RedHatAI/Qwen3-14B-FP8-dynamic
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: RedHatAI/Qwen3-14B-FP8-dynamic
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-09-20 08:06:18 +08:00
commit bd91d11f75
16 changed files with 153001 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

436
README.md Normal file
View File

@@ -0,0 +1,436 @@
---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-14B
tags:
- neuralmagic
- redhat
- llmcompressor
- quantized
- FP8
---
# Qwen3-14B-FP8-dynamic
## Model Overview
- **Model Architecture:** Qwen3ForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Activation quantization:** FP8
- **Weight quantization:** FP8
- **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:** 05/02/2025
- **Version:** 1.0
- **Model Developers:** RedHat (Neural Magic)
### Model Optimizations
This model was obtained by quantizing activations and weights of [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) to FP8 data type.
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
Weight quantization also reduces disk size requirements by approximately 50%.
Only weights and activations of the linear operators within transformers blocks are quantized.
Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
## 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/Qwen3-14B-FP8-dynamic"
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
<details>
<summary>Creation details</summary>
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 QuantizationModifier
from llmcompressor.transformers import oneshot
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model
model_stub = "Qwen/Qwen3-14B"
model_name = model_stub.split("/")[-1]
model = AutoModelForCausalLM.from_pretrained(model_stub)
tokenizer = AutoTokenizer.from_pretrained(model_stub)
# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
ignore=["lm_head"],
targets="Linear",
scheme="FP8_dynamic",
)
# Apply quantization
oneshot(
model=model,
recipe=recipe,
)
# Save to disk in compressed-tensors format
save_path = model_name + "-FP8-dynamic"
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
```
</details>
## 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.
<details>
<summary>Evaluation details</summary>
**lm-evaluation-harness**
```
lm_eval \
--model vllm \
--model_args pretrained="RedHatAI/Qwen3-14B-FP8-dynamic",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/Qwen3-14B-FP8-dynamic",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/Qwen3-14B-FP8-dynamic",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/Qwen3-14B-FP8-dynamic
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
```
</details>
### Accuracy
<table>
<tr>
<th>Category
</th>
<th>Benchmark
</th>
<th>Qwen3-14B
</th>
<th>Qwen3-14B-FP8-dynamic<br>(this model)
</th>
<th>Recovery
</th>
</tr>
<tr>
<td rowspan="7" ><strong>OpenLLM v1</strong>
</td>
<td>MMLU (5-shot)
</td>
<td>76.81
</td>
<td>76.47
</td>
<td>99.6%
</td>
</tr>
<tr>
<td>ARC Challenge (25-shot)
</td>
<td>61.60
</td>
<td>61.95
</td>
<td>100.6%
</td>
</tr>
<tr>
<td>GSM-8K (5-shot, strict-match)
</td>
<td>67.63
</td>
<td>66.19
</td>
<td>97.9%
</td>
</tr>
<tr>
<td>Hellaswag (10-shot)
</td>
<td>55.09
</td>
<td>56.46
</td>
<td>102.5%
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>62.51
</td>
<td>63.61
</td>
<td>101.8%
</td>
</tr>
<tr>
<td>TruthfulQA (0-shot, mc2)
</td>
<td>55.39
</td>
<td>55.59
</td>
<td>100.4%
</td>
</tr>
<tr>
<td><strong>Average</strong>
</td>
<td><strong>63.17</strong>
</td>
<td><strong>63.38</strong>
</td>
<td><strong>100.3%</strong>
</td>
</tr>
<tr>
<td rowspan="7" ><strong>OpenLLM v2</strong>
</td>
<td>MMLU-Pro (5-shot)
</td>
<td>44.59
</td>
<td>45.21
</td>
<td>101.6%
</td>
</tr>
<tr>
<td>IFEval (0-shot)
</td>
<td>87.48
</td>
<td>87.78
</td>
<td>100.4%
</td>
</tr>
<tr>
<td>BBH (3-shot)
</td>
<td>40.40
</td>
<td>40.37
</td>
<td>99.9%
</td>
</tr>
<tr>
<td>Math-lvl-5 (4-shot)
</td>
<td>54.18
</td>
<td>54.31
</td>
<td>100.2%
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>0.30
</td>
<td>0.00
</td>
<td>---
</td>
</tr>
<tr>
<td>MuSR (0-shot)
</td>
<td>5.74
</td>
<td>5.07
</td>
<td>---
</td>
</tr>
<tr>
<td><strong>Average</strong>
</td>
<td><strong>38.78</strong>
</td>
<td><strong>38.81</strong>
</td>
<td><strong>100.1%</strong>
</td>
</tr>
<tr>
<td><strong>Multilingual</strong>
</td>
<td>MGSM (0-shot)
</td>
<td>26.17
</td>
<td>21.77
</td>
<td>83.2%
</td>
</tr>
<tr>
<td rowspan="6" ><strong>Reasoning<br>(generation)</strong>
</td>
<td>AIME 2024
</td>
<td>76.56
</td>
<td>76.88
</td>
<td>100.4%
</td>
</tr>
<tr>
<td>AIME 2025
</td>
<td>66.35
</td>
<td>66.98
</td>
<td>101.0%
</td>
</tr>
<tr>
<td>GPQA diamond
</td>
<td>61.62
</td>
<td>64.14
</td>
<td>104.1%
</td>
</tr>
<tr>
<td>Math-lvl-5
</td>
<td>96.80
</td>
<td>97.20
</td>
<td>100.4%
</td>
</tr>
<tr>
<td>LiveCodeBench
</td>
<td>60.84
</td>
<td>60.56
</td>
<td>99.5%
</td>
</tr>
</table>