Update README.md

README.md

@@ -51,6 +51,132 @@ This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/
 vllm serve RedHatAI/Devstral-Small-2507-quantized.w4a16 --tensor-parallel-size 1 --tokenizer_mode mistral
 ```
 
+## Creation
+<details>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
+
+  ```bash
+python quantize.py --model_path mistralai/Devstral-Small-2507 --calib_size 1024 --dampening_frac 0.1 --observer mse --sym false --actorder weight
+```
+
+  ```python
+import argparse
+import os
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from mistral_common.protocol.instruct.messages import (
+    SystemMessage, UserMessage
+)
+
+def load_system_prompt(repo_id: str, filename: str) -> str:
+    file_path = os.path.join(repo_id, filename)
+    with open(file_path, "r") as file:
+        system_prompt = file.read()
+    return system_prompt
+
+def parse_actorder(value):
+    if value.lower() == "false":
+        return False
+    elif value.lower() == "weight":
+        return "weight"
+    elif value.lower() == "group":
+        return "group"
+    else:
+        raise argparse.ArgumentTypeError("Invalid value for --actorder.")
+
+def parse_sym(value):
+    if value.lower() == "false":
+        return False
+    elif value.lower() == "true":
+        return True
+    else:
+        raise argparse.ArgumentTypeError(f"Invalid value for --sym. Use false or true, but got {value}")
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str)
+parser.add_argument('--calib_size', type=int, default=256)
+parser.add_argument('--dampening_frac', type=float, default=0.1)
+parser.add_argument('--observer', type=str, default="minmax")
+parser.add_argument('--sym', type=parse_sym, default=True)
+parser.add_argument(
+    '--actorder',
+    type=parse_actorder,
+    default=False,
+    help="Specify actorder as 'group' (string) or False (boolean)."
+)
+args = parser.parse_args()
+
+
+model = AutoModelForCausalLM.from_pretrained(
+    args.model_path,
+    device_map="auto",
+    torch_dtype="auto",
+    use_cache=False,
+    trust_remote_code=True,
+)
+
+ds = load_dataset("garage-bAInd/Open-Platypus", split="train")
+ds = ds.shuffle(seed=42).select(range(args.calib_size))
+
+SYSTEM_PROMPT = load_system_prompt(args.model_path, "SYSTEM_PROMPT.txt")
+tokenizer = MistralTokenizer.from_hf_hub("mistralai/Devstral-Small-2507")
+
+def tokenize(sample):
+    tmp = tokenizer.encode_chat_completion(
+        ChatCompletionRequest(
+            messages=[
+                SystemMessage(content=SYSTEM_PROMPT),
+                UserMessage(content=sample['instruction']),
+            ],
+        )
+    )
+    return {'input_ids': tmp.tokens}
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+quant_scheme = QuantizationScheme(
+    targets=["Linear"],
+    weights=QuantizationArgs(
+        num_bits=4,
+        type=QuantizationType.INT,
+        symmetric=args.sym,
+        group_size=128,
+        strategy=QuantizationStrategy.GROUP,
+        observer=args.observer,
+        actorder=args.actorder
+    ),
+    input_activations=None,
+    output_activations=None,
+)
+
+recipe = [
+    GPTQModifier(
+        targets=["Linear"],
+        ignore=["lm_head"],
+        dampening_frac=args.dampening_frac,
+        config_groups={"group_0": quant_scheme},
+    )
+]
+
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    num_calibration_samples=args.calib_size,
+    max_seq_length=8192,
+)
+
+save_path = args.model_path + "-quantized.w4a16"
+model.save_pretrained(save_path)
+```
+</details>
+
 ## Evaluation
 
 The model was evaluated on popular coding tasks (HumanEval, HumanEval+, MBPP, MBPP+) via [EvalPlus](https://github.com/evalplus/evalplus) and vllm backend (v0.10.1.1).