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Model: RedHatAI/Phi-3-medium-128k-instruct-quantized.w8a8 Source: Original Platform
2026-09-10 08:16:12 +08:00
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+*.7z filter=lfs diff=lfs merge=lfs -text
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--- a/README.md
+++ b/README.md
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+---
+language:
+- en
+pipeline_tag: text-generation
+license: mit
+---
+
+# Phi-3-medium-128k-instruct-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Phi-3
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct), 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:** 7/11/2024
+- **Version:** 1.0
+- **License(s):** [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)
+- **Model Developers:** Neural Magic
+
+Quantized version of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct), a 14 billion-parameter open model trained using the Phi-3 datasets.
+It achieves an average score of 73.90 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 74.10.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Phi-3-medium-128k-instruct](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) to INT8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
+Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
+Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
+The [SmoothQuant](https://arxiv.org/abs/2211.10438) algorithm is used to alleviate outliers in the activations, whereas rhe [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization.
+Both algorithms are implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+GPTQ used a 1% damping factor and 512 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
+
+
+## Deployment
+
+### 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 (using 2 GPUs).
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8"
+number_gpus = 2
+
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, trust_remote_code=True, max_model_len=8196, tensor_parallel_size=number_gpus)
+
+outputs = llm.generate(prompts, 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.
+
+
+## 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 datasets import Dataset
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+import random
+
+model_id = "microsoft/Phi-3-medium-128k-instruct"
+
+num_samples = 512
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+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.shuffle().select(range(num_samples))
+ds = ds.map(preprocess_fn)
+
+recipe = [
+  SmoothQuantModifier(
+    smoothing_strength=0.8,
+    mappings=[
+      [["re:.*qkv_proj"], "re:.*input_layernorm"],
+      [["re:.*gate_up_proj"], "re:.*post_attention_layernorm"],
+    ],
+  ),
+  GPTQModifier(
+    sequential=True,
+    targets="Linear",
+    scheme="W8A8",
+    ignore=["lm_head"],
+    dampening_frac=0.01,
+    observer="mse",
+  )
+]
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  trust_remote_code=True,
+)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+)
+
+model.save_pretrained("Phi-3-medium-128k-instruct-quantized.w8a8")
+```
+
+
+
+## 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 (using 2 GPUs):
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Phi-3-medium-128k-instruct </strong>
+   </td>
+   <td><strong>Phi-3-medium-128k-instruct-quantized.w8a8 (this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>76.69
+   </td>
+   <td>76.74
+   </td>
+   <td>100.1%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>69.45
+   </td>
+   <td>69.37
+   </td>
+   <td>99.9%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>85.22
+   </td>
+   <td>84.15
+   </td>
+   <td>98.7%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>85.10
+   </td>
+   <td>84.76
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>73.56
+   </td>
+   <td>73.80
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>54.57
+   </td>
+   <td>54.57
+   </td>
+   <td>100.0%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>74.10</strong>
+   </td>
+   <td><strong>73.90</strong>
+   </td>
+   <td><strong>99.7%</strong>
+   </td>
+  </tr>
+</table>
--- a/added_tokens.json
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--- a/config.json
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+    "Phi3ForCausalLM"
+  ],
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+  }
+}
--- a/configuration.json
+++ b/configuration.json
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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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+  "transformers_version": "4.44.1"
+}
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+quant_stage:
+  quant_modifiers:
+    SmoothQuantModifier:
+      smoothing_strength: 0.8
+      mappings:
+      - - ['re:.*qkv_proj']
+        - re:.*input_layernorm
+      - - ['re:.*gate_up_proj']
+        - re:.*post_attention_layernorm
+    GPTQModifier:
+      sequential_update: true
+      dampening_frac: 0.01
+      ignore: [lm_head]
+      scheme: W8A8
+      targets: Linear
+      observer: mse
--- a/results_2024-07-10T15-10-40.715163.json
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