commit 1fe6e4cad0716bbcd3a7c8f1c274e46c6a3cc138 Author: ModelHub XC Date: Sun Jul 26 09:02:06 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ibm-granite/granite-4.1-8b-base Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..672cea4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,57 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text + +model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text + +model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text + +model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..dac6920 --- /dev/null +++ b/README.md @@ -0,0 +1,475 @@ +--- +license: apache-2.0 +library_name: transformers +tags: +- language +- granite-4.1 +--- + +[![mof-class3-qualified](https://mot.isitopen.ai/modules/mof/assets/badge_class3_qualified.png)](https://mot.isitopen.ai/model/1163) + +# Granite-4.1-8B-Base + +**Model Summary:** +Granite‑4.1‑8B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of text‑to‑text generation tasks. In addition to standard generation, it supports Fill‑in‑the‑Middle (FIM) code completion through specialized prefix and suffix tokens. The model is trained from scratch on approximately 15 trillion tokens using a five‑phase training strategy: 10 trillion tokens in phase one, 2 trillion tokens each in phases two and three, and 0.5 trillion tokens in phase four. In the final phase, long‑context extension is applied to expand the model’s context window to 512K tokens. + + +- **Developers:** Granite Team, IBM +- **HF Collection:** [Granite 4.1 Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-41-language-models) +- **Technical Blog:** [Granite-4.1 Blog](https://huggingface.co/blog/ibm-granite/granite-4-1) +- **GitHub Repository:** [ibm-granite/granite-4.1-language-models](https://github.com/ibm-granite/granite-4.1-language-models) +- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) +- **Release Date**: April 29th, 2026 +- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) + +**Supported Languages:** +English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages. + +**Intended Use:** +Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, code-completion (including FIM), and long-context generation tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios. + +**Generation:** +This is a simple example of how to use Granite-4.1-8B-Base model. + +Install the following libraries: + +```shell +pip install torch torchvision torchaudio +pip install accelerate +pip install transformers +``` +Then, copy the code snippet below to run the example. + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +device = "cuda" + +model_path = "ibm-granite/granite-4.1-8b-base" + +tokenizer = AutoTokenizer.from_pretrained(model_path) +# drop device_map if running on CPU +model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) +model.eval() +# change input text as desired +input_text = "The capital of France is" +# tokenize the text +input_tokens = tokenizer(input_text, return_tensors="pt").to(device) +# generate output tokens +output = model.generate(**input_tokens, max_length=10) +# decode output tokens into text +output = tokenizer.batch_decode(output) +# print output +print(output[0]) +``` + +Expected output: +```shell +The capital of France is Paris. +``` + +**Evaluation Results:** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarksMetric3B Dense8B Dense30B Dense
+ General Tasks +
MMLU5-shot66.4773.6078.44
MMLU-Pro5-shot,CoT37.1644.5849.51
BBH3-shot, CoT63.8473.8380.66
AGI EVAL3-shot54.3261.6869.20
DROP5-shot66.0472.3678.57
SimpleQAno-judge-short-form6.857.9210.54
+ Math Tasks +
GSM8K8-shot72.9373.5483.78
Minerva Math4-shot38.0043.4245.66
+ Code Tasks +
HumanEvalpass@1 [StarCoder Prompt]76.1979.2481.52
HumanEvalpass@159.7668.2967.68
HumanEval+pass@154.2762.8062.20
MBPPpass@181.4863.7683.60
MBPP+pass@168.2553.9769.58
Eval+ Avg65.9462.2170.76
+ Multilingual Tasks +
MMMLU5-shot56.5964.7373.36
INCLUDE5-shot51.7757.6067.07
MGSM8-shot58.4863.6874.40
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Multilingual Benchmarks and the included languages:
Benchmarks# LangsLanguages
MMMLU11ar, de, en, es, fr, ja, ko, pt, zh, bn, hi
INCLUDE14hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh
MGSM5en, es, fr, ja, zh
+ + +**Model Architecture:** + +Granite-4.1-8B-Base is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model3B Dense8B Dense30B Dense
Embedding size256040964096
Number of layers404064
Attention head size64128128
Number of attention heads403232
Number of KV heads888
MLP / Shared expert hidden size81921280032768
MLP activationSwiGLUSwiGLUSwiGLU
Sequence length131072131072131072
Position embeddingRoPERoPERoPE
# Parameters3B8B30B
+ + +**Training Data:** This model is trained on a mix of open source and proprietary data following a five-phase training strategy. We refer to phase-1 and phase-2 as pre-training and phase-3, phase-4, and phase-5 as mid-training. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StageCharacteristics3B Dense8B Dense30B Dense
IGeneral mixture of training data, warmup, and power scheduler for learning rate.10T10T10T
IIGeneral mixture of training data with higher percentages of code and math with power scheduler for learning rate.2T2T2T
IIIHigh quality training data, exponential decay of learning rate.2T2T2T
IVHigh quality training data, linear decay to zero for learning rate.500B500B500B
VLong Context Extension with exponential learning rate schedule.396B396B396B
+ + +**Infrastructure:** +We trained the Granite 4.1 Language Models utilizing an NVIDIA GB200 NVL72 cluster hosted in CoreWeave. Intra-rack communication occurs via the 72-GPU NVLink domain, and a non-blocking, full Fat-Tree NDR 400 Gb/s InfiniBand network provides inter-rack communication. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs. + +**Ethical Considerations and Limitations:** +The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Granite-4.1-8B-Base model is not an exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment and it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use Granite-4.1-8B-Base model with ethical intentions and in a responsible way. To enhance safety in enterprise deployments, we recommend using Granite 4.1 Language models alongside [Granite Guardian](https://huggingface.co/ibm-granite/granite-guardian-4.1-8b), a model designed to detect and flag risks in inputs and outputs across key dimensions outlined in the IBM AI Risk Atlas. + +**Resources** +- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite +- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/ +- 💡 Learn about the latest Granite learning resources: https://github.com/ibm-granite-community/ +- [PRISM: Demystifying Retention and Interaction in Mid-Training](https://huggingface.co/papers/2603.17074) \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..634d7cf --- /dev/null +++ b/config.json @@ -0,0 +1,32 @@ +{ + "architectures": [ + "GraniteForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + 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