Create README.md

README.md

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+---
+tags:
+- int8
+- vllm
+- chat
+- neuralmagic
+- llmcompressor
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+license: llama3.3
+base_model: meta-llama/Llama-3.3-70B-Instruct
+---
+
+# Llama-3.3-70B-Instruct-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Llama
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-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).
+- **Release Date:** 01/20/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
+It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation.
+Llama-3.3-70B-Instruct-quantized.w8a8 achieves 99.4% recovery for OpenLLM v1 (using Meta's prompting when available) and 100% for both HumanEval and HumanEval+ pass@1.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-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.
+
+## 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/Llama-3.3-70B-Instruct-quantized.w8a8"
+number_gpus = 1
+max_model_len = 8192
+
+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, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+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, AutoModelForCausalLM
+from datasets import Dataset
+from llmcompressor.transformers import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+import random
+
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+num_samples = 1024
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+max_token_id = len(tokenizer.get_vocab()) - 1
+input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
+attention_mask = num_samples * [max_seq_len * [1]]
+ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
+
+recipe = GPTQModifier(
+  targets="Linear",
+  scheme="W8A8",
+  ignore=["lm_head"],
+  dampening_frac=0.01,
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+)
+
+model.save_pretrained("Llama-3.3-70B-Instruct-quantized.w8a8")
+```
+
+## Evaluation
+
+This model was evaluated on the well-known OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks.
+In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+
+OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct).
+This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K 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) and a few fixes to OpenLLM v2 tasks.
+
+HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository.
+
+### Accuracy
+
+<table>
+  <tr>
+   <th>Category
+   </th>
+   <th>Benchmark
+   </th>
+   <th>Llama-3.3-70B-Instruct
+   </th>
+   <th>Llama-3.3-70B-Instruct-quantized.w8a8 (this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td rowspan="8" ><strong>OpenLLM v1</strong>
+   </td>
+   <td>MMLU (5-shot)
+   </td>
+   <td>81.60
+   </td>
+   <td>81.19
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (CoT, 0-shot)
+   </td>
+   <td>86.58
+   </td>
+   <td>85.92
+   </td>
+   <td>99.2%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (0-shot)
+   </td>
+   <td>49.23
+   </td>
+   <td>48.04
+   </td>
+   <td>97.6%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (CoT, 8-shot, strict-match)
+   </td>
+   <td>94.16
+   </td>
+   <td>94.01
+   </td>
+   <td>99.8%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>86.49
+   </td>
+   <td>86.47
+   </td>
+   <td>100.0%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>84.77
+   </td>
+   <td>83.74
+   </td>
+   <td>98.8%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>62.75
+   </td>
+   <td>63.09
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>77.94</strong>
+   </td>
+   <td><strong>77.49</strong>
+   </td>
+   <td><strong>99.4%</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v2</strong>
+   </td>
+   <td>MMLU-Pro (5-shot)
+   </td>
+   <td>51.89
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval (0-shot)
+   </td>
+   <td>90.89
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>BBH (3-shot)
+   </td>
+   <td>63.15
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>Math-lvl-5 (4-shot)
+   </td>
+   <td>0.17
+   </td>
+   <td>xxxx
+   </td>
+   <td>N/A
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA (0-shot)
+   </td>
+   <td>46.10
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>MuSR (0-shot)
+   </td>
+   <td>44.35
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>49.42</strong>
+   </td>
+   <td><strong>xxxx</strong>
+   </td>
+   <td><strong>xxxx%</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" ><strong>Coding</strong>
+   </td>
+   <td>HumanEval pass@1
+   </td>
+   <td>83.20
+   </td>
+   <td>83.30
+   </td>
+   <td>100.1%
+   </td>
+  </tr>
+  <tr>
+   <td>HumanEval+ pass@1
+   </td>
+   <td>78.40
+   </td>
+   <td>78.60
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="9" ><strong>Multilingual</strong>
+   </td>
+   <td>Portuguese MMLU (5-shot)
+   </td>
+   <td>79.76
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>Spanish MMLU (5-shot)
+   </td>
+   <td>79.33
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>Italian MMLU (5-shot)
+   </td>
+   <td>79.15
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>German MMLU (5-shot)
+   </td>
+   <td>77.94
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>French MMLU (5-shot)
+   </td>
+   <td>75.69
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>Hindi MMLU (5-shot)
+   </td>
+   <td>73.81
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+  <tr>
+   <td>Thai MMLU (5-shot)
+   </td>
+   <td>71.97
+   </td>
+   <td>xxxx
+   </td>
+   <td>xxxx%
+   </td>
+  </tr>
+</table>
+
+### Reproduction
+
+The results were obtained using the following commands:
+
+#### MMLU
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU-CoT
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=4064,max_gen_toks=1024,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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3940,max_gen_toks=100,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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
+  --tasks gsm8k_cot_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 8 \
+  --batch_size auto
+```
+
+#### Hellaswag
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks hellaswag \
+  --num_fewshot 10 \
+  --batch_size auto
+```
+
+#### Winogrande
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks winogrande \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### TruthfulQA
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks truthfulqa \
+  --num_fewshot 0 \
+  --batch_size auto
+```
+
+#### OpenLLM v2
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks leaderboard \
+  --batch_size auto
+```
+
+#### MMLU Portuguese
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_pt_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU Spanish
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_es_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU Italian
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_it_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU German
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_de_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU French
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_fr_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU Hindi
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_hi_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### MMLU Thai
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+  --tasks mmlu_th_llama_3.1_instruct \
+  --fewshot_as_multiturn \
+  --apply_chat_template \
+  --num_fewshot 5 \
+  --batch_size auto
+```
+
+#### HumanEval and HumanEval+
+##### Generation
+```
+python3 codegen/generate.py \
+  --model neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8 \
+  --bs 16 \
+  --temperature 0.2 \
+  --n_samples 50 \
+  --root "." \
+  --dataset humaneval
+```
+##### Sanitization
+```
+python3 evalplus/sanitize.py \
+  humaneval/neuralmagic-ent--Llama-3.3-70B-Instruct-quantized.w8a8_vllm_temp_0.2
+```
+##### Evaluation
+```
+evalplus.evaluate \
+  --dataset humaneval \
+  --samples humaneval/neuralmagic-ent--Llama-3.3-70B-Instruct-quantized.w8a8_vllm_temp_0.2-sanitized
+```