Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+base_model:
+- MiniMaxAI/MiniMax-M2.5
+tags:
+- neuralmagic
+- redhat
+- llmcompressor
+- quantized
+- INT8
+---
+
+# MiniMax-M2.5-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** MiniMaxM2ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+- **Intended Use Cases:**
+  - Reasoning.
+  - Function calling.
+  - Subject matter experts via fine-tuning.
+  - Multilingual instruction following.
+  - Translation.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 05/05/2025
+- **Version:** 1.0
+- **Model Developers:** RedHat (Neural Magic)
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) to INT8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
+A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms 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/MiniMax-M2.5-quantized.w8a8"
+number_gpus = 1
+sampling_params = SamplingParams(temperature=1.0, top_p=0.95, top_k=40, min_p=0, max_tokens=256)
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]
+
+prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
+
+outputs = llm.generate(prompts, 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 datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+
+MODEL_ID = "inference-optimization/MiniMax-M2.5-BF16"
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+NUM_CALIBRATION_SAMPLES=512
+MAX_SEQUENCE_LENGTH=2048
+
+# Load dataset.
+ds = load_dataset("HuggingFaceH4/ultrachat_200k", split=f"train_sft[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+# Preprocess the data into the format the model is trained with.
+def preprocess(example):
+    return {"text": tokenizer.apply_chat_template(example["messages"], tokenize=False)}
+
+ds = ds.map(preprocess)
+
+# Tokenize the data (be careful with bos tokens - we need add_special_tokens=False since the chat_template already added it).
+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.
+recipe = GPTQModifier( scheme="W8A8", weight_observer="mse", targets= [r"re:.*block_sparse_moe\.experts\.\d+\.w[1-3]$", r"re:.*mlp\.experts\.\d+\.(gate|up|gate_up|down)_proj$" ], ignore=["re:.*self_attn.*", "lm_head"])
+
+
+# Apply quantization.
+oneshot(
+    model=model, dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    processor=processor
+)
+
+# 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>
+ 
+
+
+
+## Evaluation
+
+The model was evaluated on the ifeval, mmlu_pro and gsm8k_platinum  using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), on reasoning tasks using [lighteval](https://github.com/neuralmagic/lighteval/tree/reasoning).
+[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.
+
+
+<details>
+  <summary>Evaluation details</summary>
+
+  Deploy using vllm to create an OpenAI-compatible API endpoint:
+
+- vLLM:
+    ```shell
+    vllm serve RedHatAI/MiniMax-M2.5.w8a8 --max-model-len 262144 --reasoning-parser deepseek_r1
+    ```
+
+  **lm-evaluation-harness**
+  ```
+  lm_eval --model local-chat-completions \
+    --tasks mmlu_pro_chat \
+    --model_args "model=RedHatAI/MiniMax-M2.5.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
+    --num_fewshot 0 \
+    --apply_chat_template \
+    --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=40,min_p=0.0,max_gen_toks=64000
+  ```
+
+  ```
+  lm_eval --model local-chat-completions \
+    --tasks ifeval \
+    --model_args "model=RedHatAI/MiniMax-M2.5.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
+    --num_fewshot 0 \
+    --apply_chat_template \
+    --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=40,min_p=0.0,max_gen_toks=64000
+  ```
+
+  ```
+  lm_eval --model local-chat-completions \
+    --tasks gsm8k_platinum_cot_llama \
+    --model_args "model=RedHatAI/MiniMax-M2.5.w8a8,max_length=262144,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=64,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
+    --num_fewshot 0 \
+    --apply_chat_template \
+    --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=40,min_p=0.0,max_gen_toks=64000
+  ```
+
+  **lighteval**
+  
+  lighteval_model_arguments.yaml
+  ```yaml 
+  model_parameters:
+    model_name: RedHatAI/MiniMax-M2.5.w8a8
+    dtype: auto
+    gpu_memory_utilization: 0.9
+    max_model_length: 40960
+    generation_parameters:
+      temperature: 1.0
+      top_k: 40
+      min_p: 0.0
+      top_p: 0.95
+      max_new_tokens: 64000
+  ```
+
+  ```
+  lighteval endpoint litellm lighteval_model_arguments.yaml  \
+    "aime25|0,math_500|0,gpqa:diamond|0"
+  ```
+
+
+</details>
+
+### Accuracy
+
+
+| Benchmark | inference-optimization/MiniMax-M2.5-BF16 | inference-optimization/MiniMax-M2.5.w8a8 | Recovery (%) |
+|-----------|------------------------------------------|-------------------------------------------|--------------|                                                                                        
+| GSM8k Platinum (0-shot) | 95.15 | 96.36 | 101.27 |                                                                                                                                                       
+| IfEval (0-shot) | 88.17 | 85.58 | 97.06 |
+| AIME 2025 | 87.50 | 84.17 | 96.19 |                                                                                                                                                                      
+| GPQA diamond | 83.67 | 84.51 | 101.01 |
+| Math 500 | 87.33 | 87.60 | 100.31 |
+| Mmlu Pro Chat | 80.83 | 81.25 | 100.51 |