Create README.md

README.md

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+---
+library_name: transformers
+language:
+- en
+- fr
+- it
+- pt
+- hi
+- es
+- th
+- de
+base_model:
+- meta-llama/Llama-3.1-70B-Instruct
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+- int4
+- quantized
+license: llama3.3
+---
+
+# Llama-3.3-70B-Instruct-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Meta-Llama-3.1
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **Intended Use Cases:** Intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
+- **Release Date:** 12/11/2024
+- **Version:** 1.0
+- **License(s):** llama3.3
+- **Model Developers:** RedHat (Neural Magic)
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) 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.
+Weights are quantized using a symmetric per-group scheme, with group size 128.
+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.
+
+## 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 = "RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16"
+number_gpus = 1
+
+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, tensor_parallel_size=number_gpus)
+
+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.
+
+## Creation
+
+<details>
+  <summary>Creation details</summary>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+  ```python
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  from llmcompressor.modifiers.quantization import GPTQModifier
+  from llmcompressor.transformers import oneshot
+  from datasets import load_dataset
+  
+  # Load model
+  model_stub = "meta-llama/Llama-3.3-70B-Instruct"
+  model_name = model_stub.split("/")[-1]
+
+  num_samples = 1024
+  max_seq_len = 8192
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+  
+  model = AutoModelForCausalLM.from_pretrained(
+      model_stub,
+      device_map="auto",
+      torch_dtype="auto",
+  )
+  
+  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.map(preprocess_fn)
+
+  # Configure the quantization algorithm and scheme
+  recipe = GPTQModifier(
+      targets="Linear",
+      scheme="W4A16",
+      ignore=["lm_head"],
+      sequential_targets=["LlamaDecoderLayer"],
+      dampening_frac=0.01,
+  )
+  
+  # Apply quantization
+  oneshot(
+      model=model,
+      dataset=ds, 
+      recipe=recipe,
+      max_seq_length=max_seq_len,
+      num_calibration_samples=num_samples,
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-quantized.w4a16"
+  model.save_pretrained(save_path)
+  tokenizer.save_pretrained(save_path)
+  print(f"Model and tokenizer saved to: {save_path}")
+  ```
+</details>
+
+## Evaluation
+
+This model was evaluated on the well-known OpenLLM v1, HumanEval, and HumanEval+ benchmarks.
+In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+
+OpenLLM v1 evaluations were conducted using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals) when available.
+
+HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository.
+
+<details>
+  <summary>Evaluation details</summary>
+
+  **MMLU**
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
+    --tasks mmlu_llama \
+    --fewshot_as_multiturn \
+    --apply_chat_template \
+    --num_fewshot 5 \
+    --batch_size auto
+  ```
+
+  **MMLU-CoT**
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \
+    --tasks mmlu_cot_llama \
+    --apply_chat_template \
+    --num_fewshot 0 \
+    --batch_size auto
+  ```
+
+  **ARC-Challenge**
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \
+    --tasks arc_challenge_llama \
+    --apply_chat_template \
+    --num_fewshot 0 \
+    --batch_size auto
+  ```
+
+  **GSM-8K**
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
+    --tasks gsm8k_llama \
+    --fewshot_as_multiturn \
+    --apply_chat_template \
+    --num_fewshot 8 \
+    --batch_size auto
+  ```
+
+  **Hellaswag**
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",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="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",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="RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
+    --tasks truthfulqa \
+    --num_fewshot 0 \
+    --batch_size auto
+  ```
+
+  **HumanEval and HumanEval+**
+  *Generation*
+  ```
+  python3 codegen/generate.py \
+    --model RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16 \
+    --bs 16 \
+    --temperature 0.2 \
+    --n_samples 50 \
+    --root "." \
+    --dataset humaneval
+  ```
+
+  *Sanitization*
+  ```
+  python3 evalplus/sanitize.py \
+    humaneval/RedHatAI--Llama-3.3-70B-Instruct-quantized.w4a16_vllm_temp_0.2
+  ```
+
+  *Evaluation*
+  ```
+  evalplus.evaluate \
+    --dataset humaneval \
+    --samples humaneval/RedHatAI--Llama-3.3-70B-Instruct-quantized.w4a16_vllm_temp_0.2-sanitized
+  ```
+</details>
+
+### Accuracy
+
+<table>
+  <tr>
+   <th>Category
+   </th>
+   <th>Benchmark
+   </th>
+   <th>Llama-3.3-70B-Instruct
+   </th>
+   <th>Llama-3.3-70B-Instruct-quantized.w4a16<br>(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>80.62
+   </td>
+   <td>98.8%
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (CoT, 0-shot)
+   </td>
+   <td>86.58
+   </td>
+   <td>85.81
+   </td>
+   <td>99.1%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (0-shot)
+   </td>
+   <td>49.23
+   </td>
+   <td>49.49
+   </td>
+   <td>100.5%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (CoT, 8-shot, strict-match)
+   </td>
+   <td>94.16
+   </td>
+   <td>94.47
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>86.49
+   </td>
+   <td>85.97
+   </td>
+   <td>99.4%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>84.77
+   </td>
+   <td>
+   </td>
+   <td>%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>62.75
+   </td>
+   <td>61.66
+   </td>
+   <td>98.3%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>77.94</strong>
+   </td>
+   <td><strong>77.49</strong>
+   </td>
+   <td><strong>98.3%</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" ><strong>Coding</strong>
+   </td>
+   <td>HumanEval pass@1
+   </td>
+   <td>83.20
+   </td>
+   <td>83.40
+   </td>
+   <td>100.2%
+   </td>
+  </tr>
+  <tr>
+   <td>HumanEval+ pass@1
+   </td>
+   <td>78.40
+   </td>
+   <td>78.60
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+</table>
+
+