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Model: JongYeop/Qwen2.5-14B-Instruct-FP8-W8A8
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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- quantization
- fp8
- compressed-tensors
- qwen2
- text-generation
- 8bit
base_model: Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-14B-Instruct-FP8-W8A8
results: []
quantization:
quant_method: compressed-tensors
bits: 8
type: float
format: float-quantized
strategy: tensor
symmetric: true
---
# Qwen2.5-14B-Instruct-FP8-W8A8
## Model Description
This is an FP8 quantized version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) using the compressed-tensors quantization method.
- **Base Model**: Qwen/Qwen2.5-14B-Instruct
- **Quantization Method**: compressed-tensors
- **Quantization Type**: FP8 W8A8 (8-bit Weight and Activation)
- **Model Size**: ~16.3GB (compared to ~28GB for BF16)
- **Compression Ratio**: ~1.7x
## Quantization Configuration
This model uses **FP8 quantization** with per-tensor quantization for both weights and activations:
### Weights
- **Precision**: FP8 (8-bit floating point)
- **Strategy**: Per-tensor
- **Group Size**: None (per-tensor)
- **Symmetric**: Yes
- **Dynamic**: No (static quantization)
- **Observer**: MinMax
### Activations
- **Precision**: FP8 (8-bit floating point)
- **Strategy**: Per-tensor
- **Group Size**: None (per-tensor)
- **Symmetric**: Yes
- **Dynamic**: No (static quantization)
- **Observer**: MinMax
### Other Details
- **Format**: float-quantized
- **KV Cache**: Not quantized
- **Ignored Layers**: lm_head
- **Target Layers**: Linear layers
- **Quantization Version**: 0.11.0
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "JongYeop/Qwen2.5-14B-Instruct-FP8-W8A8"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto"
)
# Generate text
messages = [
{"role": "user", "content": "What is machine learning?"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```
## Model Architecture
- **Architecture**: Qwen2ForCausalLM
- **Hidden Size**: 5120
- **Intermediate Size**: 13824
- **Number of Layers**: 48
- **Number of Attention Heads**: 40
- **Number of KV Heads**: 8
- **Vocabulary Size**: 152064
- **Max Position Embeddings**: 32768
## Intended Use
This quantized model is intended for efficient inference with reduced memory footprint while maintaining high accuracy. It is suitable for:
- Production deployment with reduced memory requirements
- High throughput inference scenarios
- GPU inference with FP8 support
- Applications where accuracy is important but memory savings are desired
## Limitations
- Best performance is achieved on hardware with native FP8 support (e.g., NVIDIA H100, Ada Lovelace, Blackwell GPUs)
- Requires compatible inference engines that support FP8 computation
- Static quantization may not adapt to varying input distributions
## Performance Notes
- **Memory Usage**: ~1.7x reduction compared to BF16
- **Speed**: Requires hardware with FP8 tensor core support for optimal performance
- **Accuracy**: Generally retains most of the original model's accuracy, with minimal degradation compared to FP4
## Citation
If you use this model, please cite the original Qwen2.5 paper and the compressed-tensors library.
## License
Same as the base model: [Apache 2.0](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
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"max_position_embeddings": 32768,
"max_window_layers": 70,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
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"transformers_version": "4.55.2",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "4.55.2"
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quant_stage:
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QuantizationModifier:
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