Update README.md

README.md

@@ -1,3 +1,196 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+pipeline_tag: text-generation
+license: apache-2.0
+---
+
+# SmolLM-135M-Instruct-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** SmolLM-135M-Instruct
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M), 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). Use in languages other than English.
+- **Release Date:** 8/23/2024
+- **Version:** 1.0
+- **License(s)**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Model Developers:** Neural Magic
+
+Quantized version of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M).
+It achieves an average score of 31.91 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 31.55.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M) 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. Symmetric group-wise quantization is applied, in which a linear scaling per group maps the INT4 and floating point representations of the quantized weights.
+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. Quantization is performed with 10% damping factor, group-size as 64 and 512 sequences sampled from [LLM Compression Calibration](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
+
+## Creation
+
+This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
+
+```python
+from transformers import AutoTokenizer
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy
+from datasets import load_dataset
+import random
+
+model_id = "HuggingFaceTB/SmolLM-135M-Instruct"
+
+
+num_samples = 512
+max_seq_len = 4096
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)}
+
+dataset_name = "neuralmagic/LLM_compression_calibration"
+dataset = load_dataset(dataset_name, split="train")
+ds = dataset.shuffle().select(range(num_samples))
+ds = ds.map(preprocess_fn)
+
+examples = [
+    tokenizer(
+        example["text"], padding=False, max_length=max_seq_len, truncation=True,
+    ) for example in ds
+]
+
+# recipe = "w4a16_nohead_recipe.yaml"
+recipe = GPTQModifier(
+  targets="Linear",
+  scheme="W4A16",
+  ignore=["lm_head"],
+  dampening_frac=0.1,
+)
+
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  trust_remote_code=True
+)
+
+print(model)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+  oneshot_device="cuda:1,2,3",
+)
+
+model_name = model_id.split("/")[-1]
+
+model.save_pretrained(f"{model_name}-quantized.w4a16")
+tokenizer.save_pretrained(f"{model_name}-quantized.w4a16")
+
+```
+
+
+## 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 [sparseML](https://github.com/neuralmagic/sparseml) engine, using the following command:
+```
+lm_eval \
+  --model sparseml \
+  --model_args pretrained=nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>SmolLM-135M-Instruct </strong>
+   </td>
+   <td><strong>SmolLM-135M-Instruct-quantized.w4a16(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>26.220
+   </td>
+   <td>25.202
+   </td>
+   <td>96.12%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>29.948
+   </td>
+   <td>30.034
+   </td>
+   <td>100.29%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>1.289
+   </td>
+   <td>1.971
+   </td>
+   <td>152.91%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>41.41
+   </td>
+   <td>40.81
+   </td>
+   <td>98.55%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>50.039
+   </td>
+   <td>53.591
+   </td>
+   <td>107.10%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>40.38
+   </td>
+   <td>39.87
+   </td>
+   <td>98.74%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>31.55</strong>
+   </td>
+   <td><strong>31.91</strong>
+   </td>
+   <td><strong>101.16%</strong>
+   </td>
+  </tr>
+</table>