README.md

---
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-tiny
---


# Granite-4.0-h-tiny

## 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-tiny](https://huggingface.co/ibm-granite/granite-4.0-h-tiny).

### Model Optimizations

This model was obtained by quantizing the weights and activations of [ibm-granite/granite-4.0-h-tiny](https://huggingface.co/ibm-granite/granite-4.0-h-tiny) 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 \
  --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 --no-cache

```

2. Initialize vLLM server:
```
vllm serve RedHatAI/granite-4.0-h-tiny-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-tiny-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 llm-compression 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
from llmcompressor.modeling.granite4 import pack_3d_experts

MODEL_ID = "ibm-granite/granite-4.0-h-tiny"

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)
pack_3d_experts(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 \
  --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 --no-cache
```
  
  **Openllm V1**
  ```
  lm_eval \
    --model vllm \
    --model_args pretrained="RedHatAI/granite-4.0-h-tiny-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-tiny-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-tiny-FP8-block" \
                    --dataset "humaneval" \
                    --backend vllm \
                    --tp 1 \
                    --greedy

  evalplus.evaluate --model "RedHatAI/granite-4.0-h-tiny-FP8-block" \
                  --dataset "mbpp" \
                  --backend vllm \
                  --tp 1 \
                  --greedy

  ```

</details>


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### Accuracy Comparison

| Category | Benchmark | ibm-granite/granite-4.0-h-tiny | RedHatAI/granite-4.0-h-tiny-FP8-block | Recovery (%) |
|:--|:--|:-:|:-:|:-:|
| **OpenLLM V1** | ARC-Challenge (Acc, 25-shot) | 62.97 | 63.40 | 100.68 |
| | GSM8K (Strict-Match, 5-shot) | 80.44 | 81.05 | 100.76 |
| | HellaSwag (Acc-Norm, 10-shot) | 61.75 | 61.79 | 100.06 |
| | MMLU (Acc, 5-shot) | 66.46 | 66.22 | 99.64 |
| | TruthfulQA (MC2, 0-shot) | 58.48 | 58.76 | 100.48 |
| | Winogrande (Acc, 5-shot) | 71.43 | 71.35 | 99.89 |
| | **Average** | **66.92** | **67.10** | **100.26** |
| **OpenLLM V2** | IFEval (Inst Level Strict Acc, 0-shot) | 70.62 | 69.30 | 98.13 |