zzzmahesh
/

Meta-Llama-3-8B-Instruct-quantized.w8a8

@@ -1,199 +1,88 @@
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-# Model Card for Model ID
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 ---
+tags:
+- int8
+- w8a8
+- text-generation
+language:
+- en
+pipeline_tag: text-generation
+license: llama3.1
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
 ---
+# Meta-Llama-3-8B-Instruct-quantized.w4a4
+## Model Overview
+- **Model Architecture:** Meta-Llama-3
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight and Activation Quantization:** INT8 (W8A8)
+- **Intended Use Cases:** Intended for commercial and research use across multiple languages, designed to function as an assistant-like chat model.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 9/2024
+- **Version:** 1.0
+- **License(s):** Llama3.1
+- **Model Developers:** Mahesh Yaddanapudi
+Quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). This model is optimized using weight and activation quantization to INT8, drastically reducing memory usage and enabling deployment on extremely resource-constrained environments.
+### Model Optimizations
+This model was obtained by quantizing the weights and activations of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) to INT8 (W8A8) data type. This optimization reduces the number of bits per parameter and activation from 16 to 8, significantly reducing disk size and memory requirements.
+The weights and activations of the linear operators within transformers blocks are quantized using the [GPTQ](https://arxiv.org/abs/2210.17323) algorithm, which applies symmetric per-channel quantization with a 1% damping factor and 256 sequences of 8,192 random tokens.
+## Deployment
+This model can be deployed efficiently using various backends compatible with INT8 models, as shown in the example below.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "zzzmahesh/Meta-Llama-3-8B-Instruct-quantized.w8a8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    low_cpu_mem_usage=True
+)
+prompt = "What are the benefits of model quantization in AI?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
+```
+## Creation
+This model was created by using the GPTQ quantization method as implemented in the AutoGPTQ library, as demonstrated in the code snippet below.
+```python
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+import random
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+# Create random examples for quantization calibration
+num_samples = 256
+max_seq_len = 8192
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+max_token_id = len(tokenizer.get_vocab()) - 1
+examples = [{"input_ids": [random.randint(0, max_token_id) for _ in range(max_seq_len)], "attention_mask": max_seq_len * [1]} for _ in range(num_samples)]
+# Define quantization configuration for W8A8
+quantize_config = BaseQuantizeConfig(bits=8, group_size=-1, desc_act=True, model_file_base_name="model", damp_percent=0.01)
+# Load and quantize the model
+model = AutoGPTQForCausalLM.from_pretrained(model_id, quantize_config, device_map="auto")
+model.quantize(examples)
+model.save_pretrained("Meta-Llama-3-8B-Instruct-quantized.w8a8")
+```
+## Future Work
+Further evaluations are planned to compare this quantized model with its unquantized and higher-bit quantized counterparts, especially on benchmarks relevant to code generation and logical reasoning tasks.