Update README.md

README.md

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----
-license: mit
----
+---
+tags:
+- fp8
+- vllm
+language:
+- en
+- zh
+pipeline_tag: text-generation
+base_model: zai-org/GLM-4.6
+---
+
+# GLM-4.6-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** zai-org/GLM-4.6
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **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 FP8 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-FP8-dynamic"
+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 [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a4_fp4/llama3_example.py), as presented in the code snipet below.
+
+<details>
+  
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+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", trust_remote_code=True, device_map=None
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# Configure the quantization algorithm and scheme.
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="FP8_DYNAMIC",
+    ignore = [
+        "lm_head",
+    ]
+)
+
+# Apply quantization.
+# FP8_DYNAMIC uses data-free quantization, so no calibration dataset needed
+oneshot(model=model, recipe=recipe, trust_remote_code_model=True)
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = "./" + MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-dynamic"
+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-FP8-dynamic (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>50.25%</td>
+  <td>99.21%</td>
+</tr>
+<tr>
+  <td>IFEVAL</td>
+  <td>91.97</td>
+  <td>92.69%</td>
+  <td>100.78%</td>
+</tr>
+<tr>
+  <td rowspan="6"><b>Reasoning</b></td>
+  <td>AIME25</td>
+  <td>96.67%</td>
+  <td>93.33%</td>
+  <td>96.54%<td>
+</tr>
+<tr>
+  <td>Math-500 (0-shot)</td>
+  <td>88.80%</td>
+  <td>90.40%</td>
+  <td>101.80%</%</td>
+</tr>
+<tr>
+  <td>GPQA (Diamond, 0-shot)</td>
+  <td>81.82%</td>
+  <td>77.78%</td>
+  <td>95.06%</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-FP8-dynamic,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-FP8-dynamic,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-FP8-dynamic"
+  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>