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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- fp8 |
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- quantized |
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- llm-compressor |
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- compressed-tensors |
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- red hat |
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base_model: |
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- ibm-granite/granite-4.0-h-small |
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--- |
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# Granite-4.0-h-small |
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## Model Overview |
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- **Model Architecture:** GraniteMoeHybridForCausalLM |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Release Date:** |
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- **Version:** 1.0 |
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- **Model Developers:**: Red Hat |
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Quantized version of [ibm-granite/granite-4.0-h-small](https://huggingface.co/ibm-granite/granite-4.0-h-small). |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [ibm-granite/granite-4.0-h-small](https://huggingface.co/ibm-granite/granite-4.0-h-small) to FP8 data type. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. |
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Only the weights and activations of the linear operators within transformers blocks of the language model are quantized. |
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## Deployment |
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### Use with vLLM |
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1. Install vLLM from main: |
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``` |
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uv pip install -U git+https://github.com/vllm-project/vllm.git \ |
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--extra-index-url https://wheels.vllm.ai/nightly \ |
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--no-deps \ |
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--no-cache |
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uv pip install compressed-tensors==0.12.3a20251114 --no-cache |
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uv pip install --upgrade torchvision --break-system-packages --no-cache |
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uv pip install cloudpickle msgspec zmq blake3 cachetools prometheus_client fastapi openai openai_harmony pybase64 llguidance diskcache xgrammar lm-format-enforcer partial-json-parser cbor2 einops gguf numba --no-cache |
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``` |
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2. Initialize vLLM server: |
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``` |
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vllm serve RedHatAI/granite-4.0-h-small-FP8-dynamic --tensor_parallel_size 1 |
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``` |
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3. Send requests to the server: |
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```python |
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from openai import OpenAI |
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# Modify OpenAI's API key and API base to use vLLM's API server. |
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openai_api_key = "EMPTY" |
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openai_api_base = "http://<your-server-host>:8000/v1" |
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client = OpenAI( |
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api_key=openai_api_key, |
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base_url=openai_api_base, |
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) |
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model = "RedHatAI/granite-4.0-h-small-FP8-dynamic" |
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messages = [ |
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{"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, |
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] |
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outputs = client.chat.completions.create( |
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model=model, |
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messages=messages, |
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) |
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generated_text = outputs.choices[0].message.content |
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print(generated_text) |
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``` |
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## Creation |
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This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below. |
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<details> |
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<summary>Creation details</summary> |
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Install specific version: |
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``` |
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uv pip install git+https://github.com/vllm-project/llm-compressor.git@refs/pull/2001/head --no-cache |
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uv pip install --upgrade torchvision --break-system-packages --no-cache |
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``` |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor import oneshot |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.utils import dispatch_for_generation |
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from llmcompressor.modeling import replace_modules_for_calibration |
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from llmcompressor.modeling.granite4 import pack_3d_experts |
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MODEL_ID = "ibm-granite/granite-4.0-h-small" |
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto") |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
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model = replace_modules_for_calibration(model) |
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ignore_lay = ["lm_head", "re:.*block_sparse_moe.router"] |
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recipe = QuantizationModifier( |
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targets=["Linear"], |
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scheme="FP8_DYNAMIC", |
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ignore=ignore_lay, |
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) |
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oneshot(model=model, recipe=recipe) |
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print("========== SAMPLE GENERATION ==============") |
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dispatch_for_generation(model) |
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input_ids = tokenizer( |
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"Describe Large Language Model", return_tensors="pt" |
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).input_ids.to(model.device) |
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output = model.generate(input_ids, max_new_tokens=35) |
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print(tokenizer.decode(output[0])) |
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print("==========================================") |
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SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-dynamic" |
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print(f"Saving to {SAVE_DIR}") |
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model.save_pretrained(SAVE_DIR) |
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tokenizer.save_pretrained(SAVE_DIR) |
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pack_3d_experts(SAVE_DIR) |
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``` |
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</details> |
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## Evaluation |
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The model was evaluated on the OpenLLM leaderboard task, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). |
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[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations. |
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<details> |
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<summary>Evaluation details</summary> |
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Install vLLM from main: |
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``` |
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uv pip install -U git+https://github.com/vllm-project/vllm.git \ |
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--extra-index-url https://wheels.vllm.ai/nightly \ |
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--no-deps \ |
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--no-cache |
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uv pip install compressed-tensors==0.12.3a20251114 --no-cache |
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uv pip install --upgrade torchvision --break-system-packages --no-cache |
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uv pip install cloudpickle msgspec zmq blake3 cachetools prometheus_client fastapi openai openai_harmony pybase64 llguidance diskcache xgrammar lm-format-enforcer partial-json-parser cbor2 einops gguf numba --no-cache |
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``` |
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**Openllm V1** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/granite-4.0-h-small-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=16384,tensor_parallel_size=1,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--show_config |
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``` |
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**Openllm V2** |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="RedHatAI/granite-4.0-h-small-FP8-dynamic",dtype=auto,add_bos_token=False,max_model_len=16384,tensor_parallel_size=1,gpu_memory_utilization=0.7,disable_log_stats=True,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks leaderboard \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--write_out \ |
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--batch_size auto \ |
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--show_config |
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``` |
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**Coding Benchmarks** |
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``` |
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evalplus.evaluate --model "RedHatAI/granite-4.0-h-small-FP8-dynamic" \ |
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--dataset "humaneval" \ |
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--backend vllm \ |
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--tp 1 \ |
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--greedy |
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evalplus.evaluate --model "RedHatAI/granite-4.0-h-small-FP8-dynamic" \ |
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--dataset "mbpp" \ |
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--backend vllm \ |
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--tp 1 \ |
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--greedy |
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``` |
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</details> |
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### Accuracy Comparison |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>ibm-granite/granite-4.0-h-small</th> |
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<th>ibm-granite/granite-4.0-h-small-FP8</th> |
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<th>RedHatAI/granite-4.0-h-small-FP8-block</th> |
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<th>RedHatAI/granite-4.0-h-small-FP8-dynamic</th> |
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</tr> |
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</thead> |
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<tbody> |
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<!-- OpenLLM Leaderboard V1 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>72.27</td> |
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<td>72.10 (99.76%)</td> |
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<td>72.27 (100.00%)</td> |
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<td>72.10 (99.76%)</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>85.22</td> |
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<td>85.29 (100.09%)</td> |
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<td>85.52 (100.36%)</td> |
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<td>84.84 (99.56%)</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>86.08</td> |
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<td>85.88 (99.77%)</td> |
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<td>85.96 (99.86%)</td> |
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<td>85.88 (99.77%)</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>77.15</td> |
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<td>77.18 (100.03%)</td> |
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<td>77.23 (100.09%)</td> |
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<td>77.18 (100.03%)</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>57.64</td> |
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<td>57.63 (99.99%)</td> |
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<td>57.94 (100.52%)</td> |
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<td>57.63 (100.00%)</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>81.37</td> |
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<td>81.45 (100.10%)</td> |
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<td>80.82 (99.32%)</td> |
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<td>81.45 (100.10%)</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>76.62</b></td> |
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<td><b>76.59 (99.96%)</b></td> |
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<td><b>76.62 (100.00%)</b></td> |
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<td><b>76.51 (99.86%)</b></td> |
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</tr> |
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<!-- OpenLLM Leaderboard V2 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>87.53</td> |
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<td>87.17 (99.59%)</td> |
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<td>86.69 (99.04%)</td> |
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<td>87.41 (99.86%)</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>61.52</td> |
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<td>61.31 (99.66%)</td> |
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<td>61.40 (99.80%)</td> |
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<td>61.19 (99.46%)</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>46.22</td> |
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<td>43.73 (94.61%)</td> |
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<td>43.88 (94.93%)</td> |
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<td>41.77 (90.36%)</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>35.23</td> |
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<td>34.98 (99.29%)</td> |
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<td>34.23 (97.14%)</td> |
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<td>34.23 (97.14%)</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>46.69</td> |
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<td>46.56 (99.72%)</td> |
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<td>45.77 (98.02%)</td> |
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<td>45.77 (98.02%)</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>47.99</td> |
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<td>47.63 (99.26%)</td> |
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<td>47.93 (99.88%)</td> |
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<td>47.58 (99.15%)</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>54.20</b></td> |
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<td><b>53.56 (98.82%)</b></td> |
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<td><b>53.32 (98.38%)</b></td> |
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<td><b>52.99 (97.77%)</b></td> |
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</tr> |
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</tbody> |
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</table> |
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<!-- |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>ibm-granite/granite-4.0-h-small</th> |
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<th>ibm-granite/granite-4.0-h-small-FP8</th> |
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<th>Recovery (%)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>72.27</td> |
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<td>72.10</td> |
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<td>99.76</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>85.22</td> |
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<td>85.29</td> |
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<td>100.09</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>86.08</td> |
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<td>85.88</td> |
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<td>99.77</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>77.15</td> |
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<td>77.18</td> |
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<td>100.03</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>57.64</td> |
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<td>57.63</td> |
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<td>99.99</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>81.37</td> |
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<td>81.45</td> |
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<td>100.10</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>76.62</b></td> |
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<td><b>76.59</b></td> |
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<td><b>99.96</b></td> |
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</tr> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>87.53</td> |
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<td>87.17</td> |
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<td>99.59</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>61.52</td> |
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<td>61.31</td> |
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<td>99.66</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>46.22</td> |
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<td>43.73</td> |
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<td>94.61</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>35.23</td> |
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<td>34.98</td> |
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<td>99.29</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>46.69</td> |
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<td>46.56</td> |
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<td>99.72</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>47.99</td> |
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<td>47.63</td> |
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<td>99.26</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>54.20</b></td> |
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<td><b>53.56</b></td> |
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<td><b>98.82</b></td> |
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</tr> |
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</tbody> |
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</table> |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>ibm-granite/granite-4.0-h-small</th> |
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<th>RedHatAI/granite-4.0-h-small-FP8-block</th> |
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<th>Recovery (%)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
|
|
<td>72.27</td> |
|
|
<td>72.27</td> |
|
|
<td>100.00</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>GSM8K (Strict-Match, 5-shot)</td> |
|
|
<td>85.22</td> |
|
|
<td>85.52</td> |
|
|
<td>100.36</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>HellaSwag (Acc-Norm, 10-shot)</td> |
|
|
<td>86.08</td> |
|
|
<td>85.96</td> |
|
|
<td>99.86</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MMLU (Acc, 5-shot)</td> |
|
|
<td>77.15</td> |
|
|
<td>77.23</td> |
|
|
<td>100.09</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>TruthfulQA (MC2, 0-shot)</td> |
|
|
<td>57.64</td> |
|
|
<td>57.94</td> |
|
|
<td>100.52</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Winogrande (Acc, 5-shot)</td> |
|
|
<td>81.37</td> |
|
|
<td>80.82</td> |
|
|
<td>99.32</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><b>Average Score</b></td> |
|
|
<td><b>76.62</b></td> |
|
|
<td><b>76.62</b></td> |
|
|
<td><b>100.00</b></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td rowspan="7"><b>OpenLLM V2</b></td> |
|
|
<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
|
|
<td>87.53</td> |
|
|
<td>86.69</td> |
|
|
<td>99.04</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>BBH (Acc-Norm, 3-shot)</td> |
|
|
<td>61.52</td> |
|
|
<td>61.40</td> |
|
|
<td>99.80</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Math-Hard (Exact-Match, 4-shot)</td> |
|
|
<td>46.22</td> |
|
|
<td>43.88</td> |
|
|
<td>94.93</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>GPQA (Acc-Norm, 0-shot)</td> |
|
|
<td>35.23</td> |
|
|
<td>34.23</td> |
|
|
<td>97.14</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MUSR (Acc-Norm, 0-shot)</td> |
|
|
<td>46.69</td> |
|
|
<td>45.77</td> |
|
|
<td>98.02</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MMLU-Pro (Acc, 5-shot)</td> |
|
|
<td>47.99</td> |
|
|
<td>47.93</td> |
|
|
<td>99.88</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><b>Average Score</b></td> |
|
|
<td><b>54.20</b></td> |
|
|
<td><b>53.32</b></td> |
|
|
<td><b>98.38</b></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> |
|
|
|
|
|
|
|
|
### Accuracy |
|
|
<table> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Category</th> |
|
|
<th>Metric</th> |
|
|
<th>ibm-granite/granite-4.0-h-small</th> |
|
|
<th>RedHatAI/granite-4.0-h-small-FP8-dynamic</th> |
|
|
<th>Recovery (%)</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
<tr> |
|
|
<td rowspan="7"><b>OpenLLM V1</b></td> |
|
|
<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
|
|
<td>72.27</td> |
|
|
<td>72.10</td> |
|
|
<td>99.76</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>GSM8K (Strict-Match, 5-shot)</td> |
|
|
<td>85.22</td> |
|
|
<td>84.84</td> |
|
|
<td>99.56</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>HellaSwag (Acc-Norm, 10-shot)</td> |
|
|
<td>86.08</td> |
|
|
<td>85.88</td> |
|
|
<td>99.77</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MMLU (Acc, 5-shot)</td> |
|
|
<td>77.15</td> |
|
|
<td>77.18</td> |
|
|
<td>100.03</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>TruthfulQA (MC2, 0-shot)</td> |
|
|
<td>57.64</td> |
|
|
<td>57.63</td> |
|
|
<td>100.00</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Winogrande (Acc, 5-shot)</td> |
|
|
<td>81.37</td> |
|
|
<td>81.45</td> |
|
|
<td>100.10</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><b>Average Score</b></td> |
|
|
<td><b>76.62</b></td> |
|
|
<td><b>76.51</b></td> |
|
|
<td><b>99.86</b></td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td rowspan="7"><b>OpenLLM V2</b></td> |
|
|
<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
|
|
<td>87.53</td> |
|
|
<td>87.41</td> |
|
|
<td>99.86</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>BBH (Acc-Norm, 3-shot)</td> |
|
|
<td>61.52</td> |
|
|
<td>61.19</td> |
|
|
<td>99.46</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>Math-Hard (Exact-Match, 4-shot)</td> |
|
|
<td>46.22</td> |
|
|
<td>41.77</td> |
|
|
<td>90.36</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>GPQA (Acc-Norm, 0-shot)</td> |
|
|
<td>35.23</td> |
|
|
<td>34.23</td> |
|
|
<td>97.14</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MUSR (Acc-Norm, 0-shot)</td> |
|
|
<td>46.69</td> |
|
|
<td>45.77</td> |
|
|
<td>98.02</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td>MMLU-Pro (Acc, 5-shot)</td> |
|
|
<td>47.99</td> |
|
|
<td>47.58</td> |
|
|
<td>99.15</td> |
|
|
</tr> |
|
|
<tr> |
|
|
<td><b>Average Score</b></td> |
|
|
<td><b>54.20</b></td> |
|
|
<td><b>52.99</b></td> |
|
|
<td><b>97.77</b></td> |
|
|
</tr> |
|
|
</tbody> |
|
|
</table> --> |
|
|
|
|
|
|
