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Model: RedHatAI/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16 Source: Original Platform
2026-10-02 17:13:13 +08:00
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--- a/README.md
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+---
+license: mit
+tags:
+- deepseek
+- int4
+- vllm
+- llmcompressor
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+library_name: transformers
+---
+
+# DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Qwen2ForCausalLM
+  - **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-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
+
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) 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-Qwen-1.5B-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
+
+# Load model
+model_stub = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+model_name = model_stub.split("/")[-1]
+
+num_samples = 2048
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_stub,
+    device_map="auto",
+    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-Qwen-1.5B-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-Qwen-1.5B-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-Qwen-1.5B</th>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
+      <th>Recovery</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+<td rowspan="4"><b>Reasoning</b></td>
+<td>AIME 2024 (pass@1)</td>
+<td>30.05</td>
+<td>24.63</td>
+<td>81.96%</td>
+</tr>
+<tr>
+<td>MATH-500 (pass@1)</td>
+<td>84.66</td>
+<td>85.52</td>
+<td>97.47%</td>
+</tr>
+<tr>
+<td>GPQA Diamond (pass@1)</td>
+<td>35.37</td>
+<td>33.16</td>
+<td>93.75%</td>
+</tr>
+<tr>
+<td><b>Average Score</b></td>
+<td><b>50.03</b></td>
+<td><b>46.77</b></td>
+<td><b>93.48%</b></td>
+</tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>ARC-Challenge (Acc-Norm, 25-shot)</td>
+      <td>37.20</td>
+      <td>35.84</td>
+      <td>96.3%</td>
+    </tr>
+    <tr>
+      <td>GSM8K (Strict-Match, 5-shot)</td>
+      <td>69.98</td>
+      <td>68.01</td>
+      <td>97.2%</td>
+    </tr>
+    <tr>
+      <td>HellaSwag (Acc-Norm, 10-shot)</td>
+      <td>43.86</td>
+      <td>42.38</td>
+      <td>96.6%</td>
+    </tr>
+    <tr>
+      <td>MMLU (Acc, 5-shot)</td>
+      <td>37.38</td>
+      <td>36.98</td>
+      <td>98.9%</td>
+    </tr>
+    <tr>
+      <td>TruthfulQA (MC2, 0-shot)</td>
+      <td>45.21</td>
+      <td>46.68</td>
+      <td>103.3%</td>
+    </tr>
+    <tr>
+      <td>Winogrande (Acc, 5-shot)</td>
+      <td>54.30</td>
+      <td>55.49</td>
+      <td>102.2%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>47.99</b></td>
+      <td><b>47.56</b></td>
+      <td><b>99.1%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V2</b></td>
+      <td>IFEval (Inst Level Strict Acc, 0-shot)</td>
+      <td>34.63</td>
+      <td>34.21</td>
+      <td>98.8%</td>
+    </tr>
+    <tr>
+      <td>BBH (Acc-Norm, 3-shot)</td>
+      <td>2.40</td>
+      <td>2.82</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>Math-Hard (Exact-Match, 4-shot)</td>
+      <td>0.00</td>
+      <td>0.00</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>GPQA (Acc-Norm, 0-shot)</td>
+      <td>0.93</td>
+      <td>1.19</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>MUSR (Acc-Norm, 0-shot)</td>
+      <td>1.26</td>
+      <td>1.37</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td>MMLU-Pro (Acc, 5-shot)</td>
+      <td>1.32</td>
+      <td>1.31</td>
+      <td>---</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>6.80</b></td>
+      <td><b>6.82</b></td>
+      <td><b>---</b></td>
+    </tr>
+    <tr>
+      <td rowspan="4"><b>Coding</b></td>
+      <td>HumanEval (pass@1)</td>
+      <td>37.90</td>
+      <td>35.70</td>
+      <td><b>94.2%</b></td>
+    </tr>
+    <tr>
+      <td>HumanEval (pass@10)</td>
+      <td>61.30</td>
+      <td>61.40</td>
+      <td>100.2%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>33.00</td>
+      <td>31.90</td>
+      <td>96.7%</td>
+    </tr>
+    <tr>
+      <td>HumanEval+ (pass@10)</td>
+      <td>55.90</td>
+      <td>55.50</td>
+      <td>99.3%</td>
+    </tr>
+  </tbody>
+</table>
+
+
+## Inference Performance
+
+
+This model achieves up to 1.5x speedup in single-stream deployment, depending on hardware and use-case scenario.
+The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
+
+<details>
+<summary>Benchmarking Command</summary>
+
+```
+guidellm --model neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-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 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>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">A6000x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
+      <td>---</td>
+      <td>0.8</td>
+      <td>5667</td>
+      <td>1.6</td>
+      <td>2776</td>
+      <td>0.8</td>
+      <td>5515</td>
+      <td>0.8</td>
+      <td>5466</td>
+      <td>6.4</td>
+      <td>705</td>
+      <td>6.5</td>
+      <td>697</td>
+      <td>3.5</td>
+      <td>1295</td>
+      <td>18.3</td>
+      <td>246</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8</th>
+      <td>1.14</td>
+      <td>0.7</td>
+      <td>6635</td>
+      <td>1.3</td>
+      <td>3340</td>
+      <td>0.7</td>
+      <td>6396</td>
+      <td>0.7</td>
+      <td>6343</td>
+      <td>5.3</td>
+      <td>845</td>
+      <td>5.4</td>
+      <td>832</td>
+      <td>2.9</td>
+      <td>1547</td>
+      <td>21.3</td>
+      <td>211</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
+      <td>1.38</td>
+      <td>0.5</td>
+      <td>8293</td>
+      <td>1.1</td>
+      <td>4184</td>
+      <td>0.6</td>
+      <td>7976</td>
+      <td>0.6</td>
+      <td>7504</td>
+      <td>4.3</td>
+      <td>1051</td>
+      <td>4.4</td>
+      <td>1033</td>
+      <td>2.5</td>
+      <td>1819</td>
+      <td>21.1</td>
+      <td>213</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
+      <td>---</td>
+      <td>0.6</td>
+      <td>3359</td>
+      <td>1.2</td>
+      <td>1654</td>
+      <td>0.6</td>
+      <td>3286</td>
+      <td>0.6</td>
+      <td>3241</td>
+      <td>4.7</td>
+      <td>424</td>
+      <td>4.9</td>
+      <td>411</td>
+      <td>2.6</td>
+      <td>778</td>
+      <td>21.1</td>
+      <td>95</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8</th>
+      <td>1.05</td>
+      <td>0.6</td>
+      <td>3531</td>
+      <td>1.1</td>
+      <td>1807</td>
+      <td>0.6</td>
+      <td>3427</td>
+      <td>0.6</td>
+      <td>3480</td>
+      <td>4.5</td>
+      <td>448</td>
+      <td>4.5</td>
+      <td>447</td>
+      <td>2.4</td>
+      <td>842</td>
+      <td>23.5</td>
+      <td>86</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
+      <td>1.03</td>
+      <td>0.6</td>
+      <td>3469</td>
+      <td>1.1</td>
+      <td>1751</td>
+      <td>0.6</td>
+      <td>3403</td>
+      <td>0.6</td>
+      <td>3407</td>
+      <td>4.5</td>
+      <td>447</td>
+      <td>4.6</td>
+      <td>435</td>
+      <td>2.5</td>
+      <td>815</td>
+      <td>23.3</td>
+      <td>86</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B</th>
+      <td>---</td>
+      <td>0.4</td>
+      <td>2604</td>
+      <td>0.8</td>
+      <td>1299</td>
+      <td>0.4</td>
+      <td>2543</td>
+      <td>0.4</td>
+      <td>2551</td>
+      <td>3.3</td>
+      <td>330</td>
+      <td>3.4</td>
+      <td>326</td>
+      <td>1.8</td>
+      <td>612</td>
+      <td>14.0</td>
+      <td>78</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic</th>
+      <td>1.04</td>
+      <td>0.4</td>
+      <td>2694</td>
+      <td>0.8</td>
+      <td>1364</td>
+      <td>0.4</td>
+      <td>2670</td>
+      <td>0.4</td>
+      <td>2639</td>
+      <td>3.2</td>
+      <td>347</td>
+      <td>3.2</td>
+      <td>341</td>
+      <td>1.6</td>
+      <td>673</td>
+      <td>14.1</td>
+      <td>78</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16</th>
+      <td>0.84</td>
+      <td>0.5</td>
+      <td>2111</td>
+      <td>1.0</td>
+      <td>1065</td>
+      <td>0.5</td>
+      <td>2068</td>
+      <td>0.5</td>
+      <td>2119</td>
+      <td>4.1</td>
+      <td>270</td>
+      <td>4.1</td>
+      <td>265</td>
+      <td>2.1</td>
+      <td>530</td>
+      <td>15.1</td>
+      <td>73</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).
--- a/config.json
+++ b/config.json
@@ -0,0 +1,62 @@
+{
+  "_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "quantization_config": {
+    "config_groups": {
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+        "input_activations": null,
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+        "targets": [
+          "Linear"
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+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 4,
+          "observer": "mse",
+          "observer_kwargs": {},
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int"
+        }
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+    "format": "pack-quantized",
+    "global_compression_ratio": 2.0710890309058,
+    "ignore": [
+      "lm_head"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {}
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.0",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.48.0"
+}
--- a/model.safetensors
+++ b/model.safetensors
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+version https://git-lfs.github.com/spec/v1
+oid sha256:89ea42cf4f9f651398ed53167f162f1b4cbe1cfacd1ef04baed49b2223731341
+size 1611497856
--- a/recipe.yaml
+++ b/recipe.yaml
@@ -0,0 +1,10 @@
+quant_stage:
+  quant_modifiers:
+    GPTQModifier:
+      dampening_frac: 0.1
+      ignore: [lm_head]
+      config_groups:
+        group_0:
+          weights: {num_bits: 4, type: int, symmetric: true, strategy: group, group_size: 128,
+            actorder: group, observer: mse}
+          targets: [Linear]
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
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+version https://git-lfs.github.com/spec/v1
+oid sha256:88145e3c3249adc2546ede277e9819d6e405e19072456e4b521cbc724bd60773
+size 7031660
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ac8c85fb242563c2260baec0909debd69d718af6a0b3d90e6cab62b4d341cd5
+size 3071
				`@@ -0,0 +1 @@`
				`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`