Hugging Face's logo

nm-testing
/
granite-4.0-h-small-FP8-block

Text Generation
Safetensors
granitemoehybrid
fp8
quantized
llm-compressor
compressed-tensors
red hat
conversational
Model card Files
xet
Community
granite-4.0-h-small-FP8-block / README.md
krishnateja95's picture
krishnateja95
Update README.md
8b0a0a6 verified
preview code
|
raw
history blame contribute delete
16.2 kB
 ---
license: apache-2.0
pipeline_tag: text-generation
tags:
- fp8
- quantized
- llm-compressor
- compressed-tensors
- red hat
base_model:
- ibm-granite/granite-4.0-h-small
---
# Granite-4.0-h-small
## Model Overview
- **Model Architecture:** GraniteMoeHybridForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:**
- **Version:** 1.0
- **Model Developers:**: Red Hat
Quantized version of [ibm-granite/granite-4.0-h-small](https://huggingface.co/ibm-granite/granite-4.0-h-small).
### Model Optimizations
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.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
Only the weights and activations of the linear operators within transformers blocks of the language model are quantized.
## Deployment
### Use with vLLM
1. Install specific version:
```
uv pip install -U git+https://github.com/vllm-project/vllm.git@refs/pull/28398/head \
--extra-index-url https://wheels.vllm.ai/nightly \
--no-deps \
--no-cache
uv pip install compressed-tensors==0.12.3a20251114 --no-cache
uv pip install --upgrade torchvision --break-system-packages --no-cache
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
```
2. Initialize vLLM server:
```
vllm serve RedHatAI/granite-4.0-h-small-FP8-block --tensor_parallel_size 1
```
3. Send requests to the server:
```python
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
model = "RedHatAI/granite-4.0-h-small-FP8-block"
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = client.chat.completions.create(
model=model,
messages=messages,
)
generated_text = outputs.choices[0].message.content
print(generated_text)
```
## Creation
This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below.
<details>
<summary>Creation details</summary>
Install specific version:
```
uv pip install git+https://github.com/vllm-project/llm-compressor.git@refs/pull/2001/head --no-cache
uv pip install --upgrade torchvision --break-system-packages --no-cache
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.utils import dispatch_for_generation
from llmcompressor.modeling import replace_modules_for_calibration
MODEL_ID = "ibm-granite/granite-4.0-h-small"
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = replace_modules_for_calibration(model)
ignore_lay = ["lm_head", "re:.*block_sparse_moe.router", "re:.*mamba.in_proj", "re:.*shared_mlp.input_linear"]
recipe = QuantizationModifier(
targets=["Linear"],
scheme="FP8_BLOCK",
ignore=ignore_lay,
)
oneshot(model=model, recipe=recipe)
print("========== SAMPLE GENERATION ==============")
dispatch_for_generation(model)
input_ids = tokenizer(
"Describe Large Language Model", return_tensors="pt"
).input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=35)
print(tokenizer.decode(output[0]))
print("==========================================")
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block"
print(f"Saving to {SAVE_DIR}")
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)
```
</details>
## Evaluation
The model was evaluated on the OpenLLM leaderboard task, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.
<details>
<summary>Evaluation details</summary>
Install specific version:
```
uv pip install -U git+https://github.com/vllm-project/vllm.git@refs/pull/28398/head \
--extra-index-url https://wheels.vllm.ai/nightly \
--no-deps \
--no-cache
uv pip install compressed-tensors==0.12.3a20251114 --no-cache
uv pip install --upgrade torchvision --break-system-packages --no-cache
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
```
**Openllm V1**
```
lm_eval \
--model vllm \
--model_args pretrained="RedHatAI/granite-4.0-h-small-FP8-block",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 \
--tasks openllm \
--write_out \
--batch_size auto \
--show_config
```
**Openllm V2**
```
lm_eval \
--model vllm \
--model_args pretrained="RedHatAI/granite-4.0-h-small-FP8-block",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 \
--tasks leaderboard \
--apply_chat_template \
--fewshot_as_multiturn \
--write_out \
--batch_size auto \
--show_config
```
**Coding Benchmarks**
```
evalplus.evaluate --model "RedHatAI/granite-4.0-h-small-FP8-block" \
--dataset "humaneval" \
--backend vllm \
--tp 1 \
--greedy
evalplus.evaluate --model "RedHatAI/granite-4.0-h-small-FP8-block" \
--dataset "mbpp" \
--backend vllm \
--tp 1 \
--greedy
```
</details>
<!-- <b>*</b> I/p Length = 2048, O/p Length = 2048, #Requests = 1024 -->
### Accuracy Comparison
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>ibm-granite/granite-4.0-h-small</th>
<th>ibm-granite/granite-4.0-h-small-FP8</th>
<th>RedHatAI/granite-4.0-h-small-FP8-block</th>
<th>RedHatAI/granite-4.0-h-small-FP8-dynamic</th>
</tr>
</thead>
<tbody>
<!-- OpenLLM Leaderboard V1 -->
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>72.27</td>
<td>72.10 (99.76%)</td>
<td>72.27 (100.00%)</td>
<td>72.10 (99.76%)</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>85.22</td>
<td>85.29 (100.09%)</td>
<td>85.52 (100.36%)</td>
<td>84.84 (99.56%)</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>86.08</td>
<td>85.88 (99.77%)</td>
<td>85.96 (99.86%)</td>
<td>85.88 (99.77%)</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>77.15</td>
<td>77.18 (100.03%)</td>
<td>77.23 (100.09%)</td>
<td>77.18 (100.03%)</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>57.64</td>
<td>57.63 (99.99%)</td>
<td>57.94 (100.52%)</td>
<td>57.63 (100.00%)</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>81.37</td>
<td>81.45 (100.10%)</td>
<td>80.82 (99.32%)</td>
<td>81.45 (100.10%)</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>76.62</b></td>
<td><b>76.59 (99.96%)</b></td>
<td><b>76.62 (100.00%)</b></td>
<td><b>76.51 (99.86%)</b></td>
</tr>
<!-- OpenLLM Leaderboard V2 -->
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>87.53</td>
<td>87.17 (99.59%)</td>
<td>86.69 (99.04%)</td>
<td>87.41 (99.86%)</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>61.52</td>
<td>61.31 (99.66%)</td>
<td>61.40 (99.80%)</td>
<td>61.19 (99.46%)</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>46.22</td>
<td>43.73 (94.61%)</td>
<td>43.88 (94.93%)</td>
<td>41.77 (90.36%)</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>35.23</td>
<td>34.98 (99.29%)</td>
<td>34.23 (97.14%)</td>
<td>34.23 (97.14%)</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.69</td>
<td>46.56 (99.72%)</td>
<td>45.77 (98.02%)</td>
<td>45.77 (98.02%)</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>47.99</td>
<td>47.63 (99.26%)</td>
<td>47.93 (99.88%)</td>
<td>47.58 (99.15%)</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>54.20</b></td>
<td><b>53.56 (98.82%)</b></td>
<td><b>53.32 (98.38%)</b></td>
<td><b>52.99 (97.77%)</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>ibm-granite/granite-4.0-h-small-FP8</th>
<th>RedHatAI/granite-4.0-h-small-FP8-block</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="1"><b>Model Size (GB)</b></td>
<td></td>
<td>64.41</td>
<td>33.48</td>
<td>36.43</td>
</tr>
<tr>
<td rowspan="1"><b>Throughput (Requests/sec)*</b></td>
<td></td>
<td>2.031</td>
<td>2.144</td>
<td>2.066</td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>72.27</td>
<td>71.67 (99.17)</td>
<td>72.27 (100.00)</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>85.06</td>
<td>85.60 (100.62)</td>
<td>85.60 (100.62)</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>86.07</td>
<td>86.02 (99.94)</td>
<td>85.96 (99.87)</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>77.15</td>
<td>76.94 (99.73)</td>
<td>77.23 (100.10)</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>57.97</td>
<td>57.62 (99.40)</td>
<td>57.85 (99.80)</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>81.45</td>
<td>81.14 (99.61)</td>
<td>80.82 (99.22)</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>76.66</b></td>
<td><b>76.50 (99.79)</b></td>
<td><b>76.62 (99.95)</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>87.41</td>
<td>87.65 (100.27)</td>
<td>87.89 (100.55)</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>61.52</td>
<td>61.31 (99.66)</td>
<td>61.40 (99.80)</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>46.60</td>
<td>44.34 (95.14)</td>
<td>44.94 (96.43)</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>32.55</td>
<td>32.05 (98.45)</td>
<td>34.23 (105.15)</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.43</td>
<td>46.30 (99.72)</td>
<td>45.77 (98.58)</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>47.96</td>
<td>47.91 (99.88)</td>
<td>47.93 (99.93)</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>53.75</b></td>
<td><b>53.26 (99.09)</b></td>
<td><b>53.69 (99.89)</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>ibm-granite/granite-4.0-h-small-FP8</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>71.67</td>
<td>99.17</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>85.06</td>
<td>85.60</td>
<td>100.62</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>86.07</td>
<td>86.02</td>
<td>99.94</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>77.15</td>
<td>76.94</td>
<td>99.73</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>57.97</td>
<td>57.62</td>
<td>99.40</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>81.45</td>
<td>81.14</td>
<td>99.61</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>76.66</b></td>
<td><b>76.50</b></td>
<td><b>99.79</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>87.41</td>
<td>87.65</td>
<td>100.27</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>61.52</td>
<td>61.31</td>
<td>99.66</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>46.60</td>
<td>44.34</td>
<td>95.14</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>32.55</td>
<td>32.05</td>
<td>98.45</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.43</td>
<td>46.30</td>
<td>99.72</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>47.96</td>
<td>47.91</td>
<td>99.88</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>53.75</b></td>
<td><b>53.26</b></td>
<td><b>99.09</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-block</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>72.27</td>
<td>100.00</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>85.06</td>
<td>85.60</td>
<td>100.62</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>86.07</td>
<td>85.96</td>
<td>99.87</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>77.15</td>
<td>77.23</td>
<td>100.10</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>57.97</td>
<td>57.85</td>
<td>99.80</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>81.45</td>
<td>80.82</td>
<td>99.22</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>76.66</b></td>
<td><b>76.62</b></td>
<td><b>99.95</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>87.41</td>
<td>87.89</td>
<td>100.55</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>46.60</td>
<td>44.94</td>
<td>96.43</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>32.55</td>
<td>34.23</td>
<td>105.15</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.43</td>
<td>45.77</td>
<td>98.58</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>47.96</td>
<td>47.93</td>
<td>99.93</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>53.75</b></td>
<td><b>53.69</b></td>
<td><b>99.89</b></td>
</tr>
</tbody>
</table> -->