add readme

README.md

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+---
+tags:
+- fp8
+- vllm
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B-Instruct
+---
+
+# Llama-3.2-1B-Instruct-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Meta-Llama-3.2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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:** 9/25/2024
+- **Version:** 1.0
+- **License(s):** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+Quantized version of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
+It achieves an average score of 50.88 on a subset of task from the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 51.70.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) to FP8 data type, ready for inference with vLLM built from source.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
+
+Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis.
+[LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization.
+
+## 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.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic"
+
+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, tokenize=False)
+
+llm = LLM(model=model_id)
+
+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 applying [LLM Compressor](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code snipet below.
+
+```python
+import torch
+
+from transformers import AutoTokenizer
+
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.transformers.compression.helpers import (  # noqa
+    calculate_offload_device_map,
+    custom_offload_device_map,
+)
+
+recipe = """
+quant_stage:
+    quant_modifiers:
+        QuantizationModifier:
+            ignore: ["lm_head"]
+            config_groups:
+                group_0:
+                    weights:
+                        num_bits: 8
+                        type: float
+                        strategy: channel
+                        dynamic: false
+                        symmetric: true
+                    input_activations:
+                        num_bits: 8
+                        type: float
+                        strategy: token
+                        dynamic: true
+                        symmetric: true
+                    targets: ["Linear"]
+"""
+
+model_stub = "meta-llama/Llama-3.2-1B-Instruct"
+model_name = model_stub.split("/")[-1]
+
+device_map = calculate_offload_device_map(
+    model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype="auto"
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+    model_stub, torch_dtype="auto", device_map=device_map
+)
+
+output_dir = f"./{model_name}-FP8-dynamic"
+
+oneshot(
+    model=model,
+    recipe=recipe,
+    output_dir=output_dir,
+    save_compressed=True,
+    tokenizer=AutoTokenizer.from_pretrained(model_stub),
+)
+```
+
+## Evaluation
+
+The model was evaluated on MMLU, ARC-Challenge, GSM-8K, and Winogrande.
+Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+This version of the lm-evaluation-harness includes versions of ARC-Challenge, GSM-8K, MMLU, and MMLU-cot that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals).
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Llama-3.2-1B-Instruct </strong>
+   </td>
+   <td><strong>Llama-3.2-1B-Instruct-FP8-dynamic (this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-cot (0-shot)
+   </td>
+   <td>40.17
+   </td>
+   <td>39.40
+   </td>
+   <td>98.1%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (0-shot)
+   </td>
+   <td>58.02
+   </td>
+   <td>56.74
+   </td>
+   <td>97.8%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K-cot (8-shot, strict-match)
+   </td>
+   <td>46.63
+   </td>
+   <td>45.49
+   </td>
+   <td>97.6%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>61.96
+   </td>
+   <td>61.88
+   </td>
+   <td>99.9%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>51.70</strong>
+   </td>
+   <td><strong>50.88</strong>
+   </td>
+   <td><strong>98.41%</strong>
+   </td>
+  </tr>
+</table>
+
+### Reproduction
+
+The results were obtained using the following commands:
+
+
+#### MMLU-cot
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks mmlu_cot_0shot_llama_3.1_instruct \
+  --apply_chat_template \
+  --num_fewshot 0 \
+  --batch_size auto
+```
+
+#### ARC-Challenge
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks arc_challenge_llama_3.1_instruct \
+  --apply_chat_template \
+  --num_fewshot 0 \
+  --batch_size auto
+```
+
+#### GSM-8K
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks gsm8k_cot_llama_3.1_instruct \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --num_fewshot 8 \
+  --batch_size auto
+```
+
+#### Winogrande
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks winogrande \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+