Hugging Face's logo

RedHatAI
/
DeepSeek-R1-Distill-Llama-70B-quantized.w4a16

Text Generation
Transformers
Safetensors
llama
deepseek
int4
vllm
llmcompressor
conversational
text-generation-inference
compressed-tensors
Model card Files
xet
Community
1
DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 / README.md
nm-research's picture
nm-research
Update README.md
4a48ae0 verified
preview code
|
raw
history blame
19.7 kB
 ---
license: mit
tags:
- deepseek
- int4
- vllm
- llmcompressor
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
library_name: transformers
---
# DeepSeek-R1-Distill-Llama-70B-quantized.w4a16
## Model Overview
- **Model Architecture:** LlamaForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT4
- **Release Date:** 2/7/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B).
### Model Optimizations
This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) 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 symmetric per-group scheme, with group size 128.
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.
## Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
number_gpus = 1
model_name = "neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True)
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
# Load model
model_stub = "deepseek-ai/DeepSeek-R1-Distill-Llama-70B"
model_name = model_stub.split("/")[-1]
num_samples = 3072
max_seq_len = 8192
tokenizer = AutoTokenizer.from_pretrained(model_stub)
device_map = calculate_offload_device_map(
model_stub,
reserve_for_hessians=True,
num_gpus=2,
torch_dtype="auto",
)
model = AutoModelForCausalLM.from_pretrained(
model_stub,
device_map=device_map,
torch_dtype="auto",
)
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 = QuantizationModifier(
targets="Linear",
scheme="W4A16",
ignore=["lm_head"],
dampening_frac=0.1,
)
# 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 OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
OpenLLM Leaderboard V1:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
--tasks openllm \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
OpenLLM Leaderboard V2:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
--apply_chat_template \
--fewshot_as_multiturn \
--tasks leaderboard \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<th>Recovery</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>63.65</td>
<td>63.31</td>
<td>99.5%</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>93.03</td>
<td>93.03</td>
<td>100.0%</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>84.85</td>
<td>84.43</td>
<td>99.5%</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>78.04</td>
<td>77.15</td>
<td>98.9%</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>56.67</td>
<td>57.79</td>
<td>102.0%</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>78.22</td>
<td>79.48</td>
<td>101.6%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>75.74</b></td>
<td><b>75.86</b></td>
<td><b>100.2%</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>43.15</td>
<td>42.08</td>
<td>97.5%</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>64.32</td>
<td>63.91</td>
<td>99.4%</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>35.04</td>
<td>37.81</td>
<td>107.9%</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>37.15</td>
<td>36.64</td>
<td>98.6%</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>42.89</td>
<td>42.49</td>
<td>99.1%</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>47.22</td>
<td>45.78</td>
<td>96.9%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>44.96</b></td>
<td><b>44.78</b></td>
<td><b>99.6%</b></td>
</tr>
<tr>
<td rowspan="4"><b>Coding</b></td>
<td>HumanEval (pass@1)</td>
<td>81.10</td>
<td>80.20</td>
<td><b>98.9%</b></td>
</tr>
<tr>
<td>HumanEval (pass@10)</td>
<td>87.60</td>
<td>89.30</td>
<td>101.9%</td>
</tr>
<tr>
<td>HumanEval+ (pass@10)</td>
<td>75.20</td>
<td>73.00</td>
<td>97.1%</td>
</tr>
<tr>
<td>HumanEval+ (pass@10)</td>
<td>83.10</td>
<td>83.70</td>
<td>100.7%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 3.0x speedup in single-stream deployment and up to 2.6x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.7.2)
<table>
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
</tr>
<tr>
<th>GPU class</th>
<th>Number of GPUs</th>
<th>Model</th>
<th>Average cost reduction</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
<th>Latency (s)</th>
<th>QPD</th>
</tr>
</thead>
<tbody style="text-align: center" >
<tr>
<th rowspan="3" valign="top">A6000</th>
<td>4</td>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>7.4</td>
<td>152</td>
<td>14.9</td>
<td>76</td>
<td>7.5</td>
<td>149</td>
<td>7.7</td>
<td>146</td>
<td>57.2</td>
<td>20</td>
<td>58.9</td>
<td>19</td>
<td>31.9</td>
<td>35</td>
<td>98.4</td>
<td>11</td>
</tr>
<tr>
<td>2</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th>
<td>1.93</td>
<td>7.7</td>
<td>292</td>
<td>15.2</td>
<td>148</td>
<td>7.8</td>
<td>287</td>
<td>8.0</td>
<td>282</td>
<td>60.7</td>
<td>37</td>
<td>60.2</td>
<td>37</td>
<td>32.3</td>
<td>70</td>
<td>104.0</td>
<td>22</td>
</tr>
<tr>
<td>2</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>2.83</td>
<td>4.9</td>
<td>457</td>
<td>10.0</td>
<td>225</td>
<td>5.5</td>
<td>411</td>
<td>5.8</td>
<td>389</td>
<td>38.9</td>
<td>58</td>
<td>39.2</td>
<td>57</td>
<td>23.7</td>
<td>95</td>
<td>76.6</td>
<td>29</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100</th>
<td>2</td>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>6.4</td>
<td>157</td>
<td>12.8</td>
<td>79</td>
<td>6.6</td>
<td>153</td>
<td>6.7</td>
<td>151</td>
<td>50.4</td>
<td>20</td>
<td>50.8</td>
<td>20</td>
<td>27.0</td>
<td>37</td>
<td>85.4</td>
<td>12</td>
</tr>
<tr>
<td>2</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th>
<td>1.48</td>
<td>4.1</td>
<td>245</td>
<td>8.2</td>
<td>123</td>
<td>4.2</td>
<td>238</td>
<td>4.3</td>
<td>235</td>
<td>32.4</td>
<td>31</td>
<td>32.8</td>
<td>31</td>
<td>17.6</td>
<td>57</td>
<td>90.8</td>
<td>11</td>
</tr>
<tr>
<td>1</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>2.69</td>
<td>4.6</td>
<td>440</td>
<td>9.2</td>
<td>220</td>
<td>4.9</td>
<td>407</td>
<td>5.2</td>
<td>389</td>
<td>35.3</td>
<td>57</td>
<td>36.3</td>
<td>55</td>
<td>21.2</td>
<td>95</td>
<td>68.1</td>
<td>30</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100</th>
<td>2</td>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>3.8</td>
<td>149</td>
<td>7.6</td>
<td>74</td>
<td>3.9</td>
<td>146</td>
<td>3.9</td>
<td>144</td>
<td>30.0</td>
<td>19</td>
<td>30.4</td>
<td>19</td>
<td>16.1</td>
<td>35</td>
<td>56.5</td>
<td>10</td>
</tr>
<tr>
<td>2</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic</th>
<td>1.39</td>
<td>2.7</td>
<td>210</td>
<td>5.3</td>
<td>106</td>
<td>2.7</td>
<td>207</td>
<td>2.8</td>
<td>203</td>
<td>21.1</td>
<td>27</td>
<td>21.4</td>
<td>26</td>
<td>11.5</td>
<td>49</td>
<td>47.2</td>
<td>12</td>
</tr>
<tr>
<td>1</td>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>1.83</td>
<td>4.0</td>
<td>277</td>
<td>7.9</td>
<td>138</td>
<td>4.1</td>
<td>266</td>
<td>4.2</td>
<td>262</td>
<td>31.2</td>
<td>35</td>
<td>31.8</td>
<td>34</td>
<td>17.8</td>
<td>61</td>
<td>61.4</td>
<td>18</td>
</tr>
</tbody>
</table>
**Use case profiles: prompt tokens / generation tokens
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
<table>
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Instruction Following<br>256 / 128</th>
<th style="text-align: center;" colspan="2" >Multi-turn Chat<br>512 / 256</th>
<th style="text-align: center;" colspan="2" >Docstring Generation<br>768 / 128</th>
<th style="text-align: center;" colspan="2" >RAG<br>1024 / 128</th>
<th style="text-align: center;" colspan="2" >Code Completion<br>256 / 1024</th>
<th style="text-align: center;" colspan="2" >Code Fixing<br>1024 / 1024</th>
<th style="text-align: center;" colspan="2" >Large Summarization<br>4096 / 512</th>
<th style="text-align: center;" colspan="2" >Large RAG<br>10240 / 1536</th>
</tr>
<tr>
<th>Hardware</th>
<th>Model</th>
<th>Average cost reduction</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
<th>Maximum throughput (QPS)</th>
<th>QPD</th>
</tr>
</thead>
<tbody style="text-align: center" >
<tr>
<th rowspan="3" valign="top">A6000x4</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>3.65</td>
<td>4102</td>
<td>1.56</td>
<td>1757</td>
<td>1.90</td>
<td>2143</td>
<td>1.48</td>
<td>1665</td>
<td>0.44</td>
<td>493</td>
<td>0.34</td>
<td>380</td>
<td>0.22</td>
<td>245</td>
<td>0.05</td>
<td>55</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th>
<td>1.76</td>
<td>5.89</td>
<td>6625</td>
<td>2.94</td>
<td>3307</td>
<td>3.36</td>
<td>3775</td>
<td>2.59</td>
<td>2916</td>
<td>0.74</td>
<td>828</td>
<td>0.53</td>
<td>601</td>
<td>0.35</td>
<td>398</td>
<td>0.11</td>
<td>120</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>1.48</td>
<td>4.91</td>
<td>5528</td>
<td>2.01</td>
<td>2259</td>
<td>2.03</td>
<td>2280</td>
<td>1.12</td>
<td>1255</td>
<td>1.11</td>
<td>1251</td>
<td>0.76</td>
<td>852</td>
<td>0.24</td>
<td>267</td>
<td>0.07</td>
<td>81</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x4</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>10.41</td>
<td>5235</td>
<td>5.10</td>
<td>2565</td>
<td>5.50</td>
<td>2766</td>
<td>4.36</td>
<td>2193</td>
<td>1.49</td>
<td>751</td>
<td>1.21</td>
<td>607</td>
<td>0.89</td>
<td>447</td>
<td>0.19</td>
<td>98</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w8a8</th>
<td>1.63</td>
<td>18.11</td>
<td>9103</td>
<td>8.90</td>
<td>4477</td>
<td>9.41</td>
<td>4730</td>
<td>7.42</td>
<td>3731</td>
<td>2.44</td>
<td>1229</td>
<td>1.89</td>
<td>948</td>
<td>1.26</td>
<td>631</td>
<td>0.30</td>
<td>149</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>1.12</td>
<td>12.63</td>
<td>6353</td>
<td>5.32</td>
<td>2673</td>
<td>5.58</td>
<td>2804</td>
<td>4.27</td>
<td>2144</td>
<td>2.30</td>
<td>1158</td>
<td>1.45</td>
<td>729</td>
<td>0.76</td>
<td>381</td>
<td>0.22</td>
<td>110</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x4</th>
<th>deepseek-ai/DeepSeek-R1-Distill-Llama-70B</th>
<td>---</td>
<td>14.04</td>
<td>2113</td>
<td>10.85</td>
<td>1634</td>
<td>12.25</td>
<td>1844</td>
<td>9.93</td>
<td>1494</td>
<td>3.68</td>
<td>554</td>
<td>2.82</td>
<td>425</td>
<td>1.81</td>
<td>273</td>
<td>0.35</td>
<td>52</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic</th>
<td>1.78</td>
<td>41.44</td>
<td>6236</td>
<td>19.64</td>
<td>2956</td>
<td>21.03</td>
<td>3166</td>
<td>16.72</td>
<td>2516</td>
<td>6.01</td>
<td>904</td>
<td>4.46</td>
<td>672</td>
<td>2.55</td>
<td>383</td>
<td>0.49</td>
<td>74</td>
</tr>
<tr>
<th>neuralmagic/DeepSeek-R1-Distill-Llama-70B-quantized.w4a16</th>
<td>1.45</td>
<td>36.61</td>
<td>5509</td>
<td>15.12</td>
<td>2275</td>
<td>16.24</td>
<td>2443</td>
<td>13.22</td>
<td>1990</td>
<td>5.48</td>
<td>825</td>
<td>3.01</td>
<td>453</td>
<td>2.07</td>
<td>312</td>
<td>0.43</td>
<td>64</td>
</tr>
</tbody>
</table>
**Use case profiles: prompt tokens / generation tokens
**QPS: Queries per second.
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).