| --- |
| tags: |
| - w8a8 |
| - vllm |
| language: |
| - en |
| - zh |
| pipeline_tag: text-generation |
| base_model: zai-org/GLM-4.6 |
| --- |
| |
| # GLM-4.6-quantized.w8a8 |
|
|
| ## Model Overview |
| - **Model Architecture:** zai-org/GLM-4.6 |
| - **Input:** Text |
| - **Output:** Text |
| - **Model Optimizations:** |
| - **Weight quantization:** INT8 |
| - **Activation quantization:** INT8 |
| - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. |
| - **Version:** 1.0 |
| - **Model Developers:** RedHatAI |
|
|
| This model is a quantized version of [zai-org/GLM-4.6](https://huggingface.co/zai-org/GLM-4.6). |
| It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. |
|
|
| ### Model Optimizations |
|
|
| This model was obtained by quantizing the weights and activations of [zai-org/GLM-4.6](https://huggingface.co/zai-org/GLM-4.6) to INT8 data type, ready for inference with vLLM>=0.11.0. |
|
|
| Only the weights and activations of the linear operators within transformers blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor). |
|
|
| ## Deployment |
|
|
| ### Use with vLLM |
|
|
| 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/GLM-4.6-quantized.w8a8" |
| number_gpus = 4 |
| |
| sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| |
| messages = [ |
| {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, |
| {"role": "user", "content": "Who are you?"}, |
| ] |
| |
| 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 |
|
|
| This model was created by applying a script similar to [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantizing_moe/glm4_7_example.py), as presented in the code snipet below. |
|
|
| <details> |
| |
| ```python |
| from datasets import load_dataset |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| from llmcompressor import oneshot |
| from llmcompressor.modifiers.quantization import GPTQModifier |
| from llmcompressor.utils import dispatch_for_generation |
| |
| MODEL_ID = "zai-org/GLM-4.6" |
| |
| # Load model. |
| model = AutoModelForCausalLM.from_pretrained( |
| MODEL_ID, torch_dtype="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| |
| # Select calibration dataset. |
| DATASET_ID = "HuggingFaceH4/ultrachat_200k" |
| DATASET_SPLIT = "train_sft" |
| |
| # Select number of samples. |
| # Increasing the number of samples can improve accuracy. |
| NUM_CALIBRATION_SAMPLES = 512 |
| MAX_SEQUENCE_LENGTH = 2048 |
| |
| # 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( |
| example["messages"], |
| 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 and scheme with explicit parameters. |
| recipe = GPTQModifier( |
| targets="Linear", |
| scheme="W8A8", |
| ignore=[ |
| "lm_head", |
| "re:.*mlp.gate$" |
| ], |
| ) |
| |
| # Apply quantization. |
| oneshot( |
| model=model, |
| dataset=ds, |
| recipe=recipe, |
| max_seq_length=MAX_SEQUENCE_LENGTH, |
| num_calibration_samples=NUM_CALIBRATION_SAMPLES, |
| pipeline="sequential", |
| sequential_targets=["Glm4MoeDecoderLayer"], |
| trust_remote_code_model=True, |
| ) |
| |
| SAVE_DIR = "./" + MODEL_ID.rstrip("/").split("/")[-1] + "-quantized.w8a8" |
| model.save_pretrained(SAVE_DIR, save_compressed=True) |
| tokenizer.save_pretrained(SAVE_DIR) |
| |
| ``` |
| </details> |
|
|
| ## Evaluation |
|
|
| This model was evaluated on the well-known text benchmarks using [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness). The Reasoning evals were done using [ligheval](https://github.com/neuralmagic/lighteval). |
|
|
| ### Accuracy |
|
|
| <table> |
| <thead> |
| <tr> |
| <th>Category</th> |
| <th>Metric</th> |
| <th>zai-org/GLM-4.6-FP8</th> |
| <th>RedHatAI/GLM-4.6-quantized.w8a8 (this model)</th> |
| <th>Recovery</th> |
| </tr> |
| </thead> |
| <tbody> |
| <!-- OpenLLM V1 --> |
| <tr> |
| <td rowspan="2"><b>Leaderboard</b></td> |
| <td>MMLU Pro</td> |
| <td>50.65%</td> |
| <td>50.08%</td> |
| <td>98.87%</td> |
| </tr> |
| <tr> |
| <td>IFEVAL</td> |
| <td>91.97%</td> |
| <td>93.68%</td> |
| <td>101.86%</td> |
| </tr> |
| <tr> |
| <td rowspan="6"><b>Reasoning</b></td> |
| <td>AIME25</td> |
| <td>96.67%</td> |
| <td>90.00%</td> |
| <td>93.10%</td> |
| </tr> |
| <tr> |
| <td>Math-500 (0-shot)</td> |
| <td>88.80%</td> |
| <td>90.60%</td> |
| <td>102.03%</td> |
| </tr> |
| <tr> |
| <td>GPQA (Diamond, 0-shot)</td> |
| <td>81.82%</td> |
| <td>78.78%</td> |
| <td>96.28%</td> |
| </tr> |
| </tbody> |
| </table> |
| |
|
|
| ### Reproduction |
|
|
| The results were obtained using the following commands: |
|
|
| <details> |
|
|
| #### Leaderboard |
|
|
| ``` |
| lm_eval --model local-chat-completions \ |
| --tasks mmlu_pro \ |
| --model_args "model=RedHatAI/GLM-4.6-quantized.w8a8,max_length=90000,base_url=http://0.0.0.0:3758/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \ |
| --num_fewshot 5 \ |
| --apply_chat_template \ |
| --fewshot_as_multiturn \ |
| --output_path ./ \ |
| --seed 42 \ |
| --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,max_gen_toks=64000" |
| |
| |
| lm_eval --model local-chat-completions \ |
| --tasks leaderboard_ifeval \ |
| --model_args "model=RedHatAI/GLM-4.6-quantized.w8a8,max_length=90000,base_url=http://0.0.0.0:3758/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \ |
| --num_fewshot 5 \ |
| --apply_chat_template \ |
| --fewshot_as_multiturn \ |
| --output_path ./ \ |
| --seed 42 \ |
| --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,max_gen_toks=64000" |
| ``` |
|
|
|
|
| #### Reasoning |
| ``` |
| litellm_config.yaml: |
| |
| model_parameters: |
| provider: "hosted_vllm" |
| model_name: "hosted_vllm/redhatai-glm-4.6-w8a8" |
| base_url: "http://0.0.0.0:3759/v1" |
| api_key: "" |
| timeout: 3600 |
| concurrent_requests: 128 |
| generation_parameters: |
| temperature: 1.0 |
| max_new_tokens: 131072 |
| top_p: 0.95 |
| seed: 0 |
| |
| lighteval endpoint litellm litellm_config.yaml \ |
| "aime25|0,math_500|0,gpqa:diamond|0" \ |
| --output-dir ./ \ |
| --save-details |
| ``` |
|
|
| </details> |
