Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
+tags:
+- quantized
+- w8a8
+- llm-compressor
+---
+
+```
+    ██╗    ██╗ █████╗   █████╗  █████╗ 
+    ██║    ██║██╔══██╗ ██╔══██╗██╔══██╗
+    ██║ █╗ ██║╚█████╔╝ ███████║╚█████╔╝
+    ██║███╗██║██╔══██╗ ██╔══██║██╔══██╗
+    ╚███╔███╔╝╚█████╔╝ ██║  ██║╚█████╔╝
+     ╚══╝╚══╝  ╚════╝  ╚═╝  ╚═╝ ╚════╝ 
+        🗜️ COMPRESSED & OPTIMIZED 🚀
+```
+
+# Qwen3-Coder-30B-A3B-Instruct - W8A8 Quantized
+
+W8A8 (8-bit weights and activations) quantized version of Qwen/Qwen3-Coder-30B-A3B-Instruct using **LLM-Compressor**.
+
+- 🗜️ **Memory**: ~50% reduction vs FP16  
+- 🚀 **Speed**: Faster inference on supported hardware  
+- 🔗 **Original model**: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct  
+- 🏗️ **Recommended architectures**: Ampere and older
+
+<details>
+<summary>Click to view compression config</summary>
+
+```python
+cat Qwen3-Coder-30B-A3B-w8a8-Instruct.py
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.utils import dispatch_for_generation
+from llmcompressor.modifiers.quantization import QuantizationModifier
+# Select model and load it.
+model_id = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+    low_cpu_mem_usage=True,
+    offload_folder="./offload_tmp",  # Add offload directory
+    max_memory={0: "22GB", 1: "22GB", "cpu": "64GB"},
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+# Select calibration dataset.
+DATASET_ID = "mit-han-lab/pile-val-backup"
+DATASET_SPLIT = "validation"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 256
+MAX_SEQUENCE_LENGTH = 512
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            [{"role": "user", "content": example["text"]}],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+# Configure the quantization algorithm to run.
+#   * quantize the activations to int8 (dynamic per token)
+recipe = QuantizationModifier(targets="Linear", scheme="W8A8", ignore=["lm_head", "re:.*mlp.gate$"])
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    output_dir="./Qwen/Qwen3-Coder-30B-A3B-Instruct-W8A8",  # Add this line
+)
+
+
+# Save to disk compressed.
+SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W8A8"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+
+</details>
+
+---
+
+## 📄 Original Model README
+
+# Qwen3-Coder-30B-A3B-Instruct
+<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Highlights
+
+**Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements:  
+
+- **Significant Performance** among open models on **Agentic Coding**, **Agentic Browser-Use**, and other foundational coding tasks.
+- **Long-context Capabilities** with native support for **256K** tokens, extendable up to **1M** tokens using Yarn, optimized for repository-scale understanding.
+- **Agentic Coding** supporting for most platform such as **Qwen Code**, **CLINE**, featuring a specially designed function call format.
+
+![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Coder/qwen3-coder-30a3-main.jpg)
+
+## Model Overview
+
+**Qwen3-Coder-30B-A3B-Instruct** has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Number of Parameters: 30.5B in total and 3.3B activated
+- Number of Layers: 48
+- Number of Attention Heads (GQA): 32 for Q and 4 for KV
+- Number of Experts: 128
+- Number of Activated Experts: 8
+- Context Length: **262,144 natively**. 
+
+**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
+
+For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+
+## Quickstart
+
+We advise you to use the latest version of `transformers`.
+
+With `transformers<4.51.0`, you will encounter the following error:
+```
+KeyError: 'qwen3_moe'
+```
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Write a quick sort algorithm."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
+
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
+
+## Agentic Coding
+
+Qwen3-Coder excels in tool calling capabilities. 
+
+You can simply define or use any tools as following example.
+```python
+# Your tool implementation
+def square_the_number(num: float) -> dict:
+    return num ** 2
+
+# Define Tools
+tools=[
+    {
+        "type":"function",
+        "function":{
+            "name": "square_the_number",
+            "description": "output the square of the number.",
+            "parameters": {
+                "type": "object",
+                "required": ["input_num"],
+                "properties": {
+                    'input_num': {
+                        'type': 'number', 
+                        'description': 'input_num is a number that will be squared'
+                        }
+                },
+            }
+        }
+    }
+]
+
+import OpenAI
+# Define LLM
+client = OpenAI(
+    # Use a custom endpoint compatible with OpenAI API
+    base_url='http://localhost:8000/v1',  # api_base
+    api_key="EMPTY"
+)
+ 
+messages = [{'role': 'user', 'content': 'square the number 1024'}]
+
+completion = client.chat.completions.create(
+    messages=messages,
+    model="Qwen3-Coder-30B-A3B-Instruct",
+    max_tokens=65536,
+    tools=tools,
+)
+
+print(completion.choice[0])
+```
+
+## Best Practices
+
+To achieve optimal performance, we recommend the following settings:
+
+1. **Sampling Parameters**:
+   - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`.
+
+2. **Adequate Output Length**: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models.
+
+
+### Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen3technicalreport,
+      title={Qwen3 Technical Report}, 
+      author={Qwen Team},
+      year={2025},
+      eprint={2505.09388},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.09388}, 
+}
+```