Update README.md

README.md

@@ -1,199 +1,253 @@
 ---
-library_name: transformers
-tags: []
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# Qwen2-0.5B-Instruct-quantized.w8a16
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-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:** 7/2/2024
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
 
+Quantized version of [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
+It achieves an average score of 43.06 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 42.99.
 
+### Model Optimizations
 
-## Model Details
+This model was obtained by quantizing the weights of [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) to INT8 data type.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
 
-### Model Description
+Only the weights 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 INT8 and floating point representations of the quantized weights.
+[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 1% damping factor and 256 sequences of 8,192 random tokens.
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Deployment
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Use with vLLM
 
-### Model Sources [optional]
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
 
-<!-- Provide the basic links for the model. -->
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+model_id = "neuralmagic/Qwen2-0.5B-Instruct-quantized.w8a16"
 
-## Uses
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-### Direct Use
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+prompts = tokenizer.apply_chat_template(messages, tokenize=False)
 
-[More Information Needed]
+llm = LLM(model=model_id)
 
-### Downstream Use [optional]
+outputs = llm.generate(prompts, sampling_params)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
 
-[More Information Needed]
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
 
-### Out-of-Scope Use
+### Use with transformers
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model is supported by Transformers leveraging the integration with the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) data format.
+The following example contemplates how the model can be used using the `generate()` function.
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Bias, Risks, and Limitations
+model_id = "neuralmagic/Qwen2-0.5B-Instruct-quantized.w8a16"
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
 
-[More Information Needed]
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
 
-### Recommendations
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.8,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+```
 
-## How to Get Started with the Model
+## Creation
 
-Use the code below to get started with the model.
+This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below.
+Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ.
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import random
 
-## Training Details
+model_id = "Qwen/Qwen2-0.5B-Instruct"
 
-### Training Data
+num_samples = 256
+max_seq_len = 8192
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-[More Information Needed]
+max_token_id = len(tokenizer.get_vocab()) - 1
+examples = []
+for _ in range(num_samples):
+  examples.append(
+  {
+    "input_ids": [random.randint(0, max_token_id) for _ in range(max_seq_len)],
+    "attention_mask": max_seq_len*[1],
+  }
+)
 
-### Training Procedure
+quantize_config = BaseQuantizeConfig(
+  bits=8,
+  group_size=-1,
+  desc_act=False,
+  model_file_base_name="model",
+  damp_percent=0.01,
+)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+model = AutoGPTQForCausalLM.from_pretrained(
+  model_id,
+  quantize_config,
+  device_map="auto",
+)
 
-#### Preprocessing [optional]
+model.quantize(examples)
+model.save_pretrained("Meta-Llama-3-8B-Instruct-quantized.w8a16")
+```
 
-[More Information Needed]
 
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2-0.5B-Instruct-quantized.w8a16",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Qwen2-0.5B-Instruct</strong>
+   </td>
+   <td><strong>Qwen2-0.5B-Instruct-quantized.w8a16(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>43.72
+   </td>
+   <td>43.85
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>31.83
+   </td>
+   <td>31.74
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>37.68
+   </td>
+   <td>38.06
+   </td>
+   <td>101.0%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>49.50
+   </td>
+   <td>49.42
+   </td>
+   <td>99.8%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>56.27
+   </td>
+   <td>55.64
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>39.38
+   </td>
+   <td>39.24
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>43.06</strong>
+   </td>
+   <td><strong>42.99</strong>
+   </td>
+   <td><strong>99.8%</strong>
+   </td>
+  </tr>
+</table>