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Model: Vcecca/mamba-50m
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- mamba
+- state-space-model
+- ssm
+- causal-lm
+- pytorch
+- pretrained
+datasets:
+- wikimedia/wikipedia
+---
+
+# Mamba-50M
+
+A ~50M-parameter [Mamba](https://arxiv.org/abs/2312.00752) (selective state-space) causal
+language model, **pretrained from scratch on English Wikipedia**. Mamba replaces the attention
+mechanism of a Transformer with a selective state-space layer, giving linear-time sequence
+processing instead of the quadratic cost of self-attention.
+
+> **This is a base model: pretrained only.** It has **not** been fine-tuned, instruction-tuned,
+> RLHF'd, or aligned in any way. It is a raw next-token predictor intended for research.
+
+## Model details
+
+| | |
+|---|---|
+| Architecture | Mamba (selective SSM) |
+| Parameters | ~50M |
+| Context length | 512 tokens |
+| Tokenizer | [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b) (BPE, ~50k vocab) — the same tokenizer used by the original `state-spaces/mamba-*` models |
+| Language | English |
+| License | Apache-2.0 |
+
+## Limitations
+
+- **Pretrained only.** No fine-tuning, instruction tuning, or alignment. It does not follow instructions and has no safety filtering; it simply continues text.
+- **Small.** At ~50M parameters it has limited fluency and reasoning; expect frequent hallucination and repetition.
+- **English only.** Trained solely on English Wikipedia; other languages are out of distribution.
+- **Domain-narrow.** Only Wikipedia was used as training data.
+
+## Training data
+
+Pretrained on the English subset of Wikipedia: over 3 million articles.
+
+### Training procedure
+
+| Hyperparameter | Value |
+|---|---|
+| Learning rate | 5e-4 |
+| Sequence length | 512 |
+| Batch size | 64 |
+| Tokenizer | GPT-NeoX-20B (`EleutherAI/gpt-neox-20b`) |
+| Optimizer | `AdamW` |
+| LR schedule / warmup | `constant` / `10000` |
+| Total tokens seen | `~ 2.5-2.9B` |
+| Hardware | `2x Nvidia Quadro RTX 6000 24GB` |
+
+## Evaluation
+
+Evaluated on a held-out set of **10,000 Wikipedia articles** that were not seen during training.
+The training and evaluation loss curves are shown below.
+
+![Training and evaluation loss](loss_curves.png)
+
+## Citation
+
+If you use this model, please cite the Mamba paper:
+
+```bibtex
+@article{gu2023mamba,
+  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
+  author={Gu, Albert and Dao, Tri},
+  journal={arXiv preprint arXiv:2312.00752},
+  year={2023}
+}
+```

config.json

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+{
+  "_name_or_path": "./checkpoints/snapshot-articles-3180018",
+  "architectures": [
+    "MambaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "conv_kernel": 4,
+  "eos_token_id": 0,
+  "expand": 2,
+  "hidden_act": "silu",
+  "hidden_size": 384,
+  "initializer_range": 0.1,
+  "intermediate_size": 768,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "mamba",
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rescale_prenorm_residual": false,
+  "residual_in_fp32": true,
+  "state_size": 16,
+  "time_step_floor": 0.0001,
+  "time_step_init_scheme": "random",
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "time_step_rank": 24,
+  "time_step_scale": 1.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "use_bias": false,
+  "use_cache": false,
+  "use_conv_bias": true,
+  "use_mambapy": false,
+  "vocab_size": 50280
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "pad_token_id": 0,
+  "transformers_version": "4.48.0",
+  "use_cache": false
+}

loss_curves.png

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special_tokens_map.json

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+{
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "add_prefix_space": false,
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+}