AMD-Llama-135m is a language model trained on AMD Instinct MI250 accelerators. Based on LLama2 model architecture, this model can be smoothly loaded as LlamaForCausalLM with huggingface transformers. Furthermore, we use the same tokenizer as LLama2, enabling it to be a draft model of speculative decoding for LLama2 and CodeLlama. AMD-Llama-135m and AMD-Llama-135m-code can be loaded and used via huggingface transformers, here is a simple example. We use SlimPajama and project gutenberg dataset to pretrain our 135m model, around 670B training tokens in total. SlimPajama is a deduplicated version of RedPajama and sources from Commoncrawl, C4, GitHub, Books, ArXiv, Wikpedia and StackExchange. We droped the Books data from SlimPajama due to license issues and used project gutenberg dataset instead.
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- **Model Architecture:** DeepSeek-R1 - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm**: 7.0 - **PyTorch**: 2.8.0 - **Transformers**: 4.53.0 - **Operating System(s):** Linux - **Inference Engine:** SGLang - **Model Optimizer:** AMD-Quark (V0.10) - **Weight quantization:** OCP MXFP4, Static - **Activation quantization:** OCP MXFP4, Dynamic - **KV cache**: OCP FP8, Static - **Calibration Dataset:** Pile This model was built with deepseek-ai DeepSeek-R1 model by applying AMD-Quark for MXFP4 quantization. The model was quantized from deepseek-ai/DeepSeek-R1 using AMD-Quark. Both weights and activations were quantized to MXFP4 format.
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- **Model Architecture:** GLM-5.2 - **Input:** Text - **Output:** Text - **Supported Hardware Microarchitecture:** AMD MI350/MI355 - **ROCm:** 7.0.0 - **PyTorch:** 2.9.0 - **Transformers:** 5.8.1 - **Operating System(s):** Linux - **Inference Engine:** SGLang/vLLM - **Model Optimizer:** AMD-Quark (V0.11) - **Weight quantization:** MOE-only (shared experts quantized), OCP MXFP4, Static - **Activation quantization:** MOE-only, OCP MXFP4, Dynamic This model was built with GLM-5.2 model by applying AMD-Quark for MXFP4 quantization. The model was quantized from zai-org/GLM-5.2 using AMD-Quark. The weights and activations are quantized to MXFP4.
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GLM-5-MXFP4
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The model was quantized from openai/gpt-oss-120b using AMD-Quark. The weights are quantized MXFP4 and activations were quantized to FP8. The results of AIME25 and GPQA Diamond were obtained using gpt_oss.evals with `low` effort setting, and vLLM docker `rocm/vllm-private:rocm7.0_ubuntu_22.04_vllm_0.10.1_instinct_gptoss_wmxfp4_afp8_20251030`.
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