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Model: RedHatAI/Qwen2.5-1.5B-quantized.w4a16
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B
+tags:
+- chat
+- neuralmagic
+- llmcompressor
+---
+
+# Qwen2.5-1.5B-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 12/17/2024
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B).
+It achieves an average score of 56.69 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 58.48.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) 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 weights of the linear operators within transformers blocks are quantized.
+Symmetric per-group quantization is applied, in which a linear scaling per group of 128 parameters maps the INT4 and floating point representations of the quantized weights.
+
+## 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 = "neuralmagic-ent/Qwen2.5-1.5B-quantized.w4a16"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt = "Give me a short introduction to large language model."
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompt, 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.
+
+
+## Evaluation
+
+The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Qwen2.5-1.5B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.9,add_bos_token=True,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Qwen2.5-1.5B</strong>
+   </td>
+   <td><strong>Qwen2.5-1.5B-quantized.w4a16 (this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>60.98
+   </td>
+   <td>58.93
+   </td>
+   <td>96.6%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>49.66
+   </td>
+   <td>47.70
+   </td>
+   <td>96.1%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>60.96
+   </td>
+   <td>56.71
+   </td>
+   <td>93.0%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>67.65
+   </td>
+   <td>66.79
+   </td>
+   <td>98.7%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>65.04
+   </td>
+   <td>65.51
+   </td>
+   <td>100.7%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>46.57
+   </td>
+   <td>44.49
+   </td>
+   <td>95.5%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>58.48</strong>
+   </td>
+   <td><strong>56.69</strong>
+   </td>
+   <td><strong>96.9%</strong>
+   </td>
+  </tr>
+</table>
+

added_tokens.json

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config.json

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+{
+  "_name_or_path": "/root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B/snapshots/8faed761d45a263340a0528343f099c05c9a4323",
+  "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": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "quantization_config": {
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+        "output_activations": null,
+        "targets": [
+          "Linear"
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+        "weights": {
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+          "block_structure": null,
+          "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": 1.889251557273008,
+    "ignore": [
+      "lm_head"
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+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {}
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.45.2"
+}

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recipe.yaml

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+quant_stage:
+  quant_modifiers:
+    GPTQModifier:
+      sequential_update: true
+      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]

special_tokens_map.json

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+    "<|im_end|>",
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