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Model: BananaMind/MiniBananaMind-V1
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- HuggingFaceFW/fineweb-edu
+tags:
+- llama
+- causal-lm
+- fineweb-edu
+- small-language-model
+- text-generation
+widget:
+- text: "A computer is a machine that"
+- text: "I like traveling by train because"
+- text: "Once upon a time,"
+---
+
+# MiniBananaMind-V1
+
+![MiniBananaMind-V1 preview](assets/minibananamind-v1.png)
+
+MiniBananaMind-V1 is a compact LLaMA-style causal language model from BananaMind.
+It is trained from scratch for next-token text generation on streamed
+FineWeb-Edu data and is intended as a small, inspectable base model for
+experiments, demos, and lightweight research workflows.
+
+This is a base language model, not an instruction-tuned assistant. It is best
+used for continuation-style generation and experimentation rather than factual
+question answering or chat.
+
+## Model Details
+
+- **Developer:** BananaMind
+- **Model type:** LLaMA-style causal language model
+- **Library:** Transformers
+- **Task:** Text generation
+- **Training data:** FineWeb-Edu
+- **Checkpoint:** MiniBananaMind-V1 uploaded training checkpoint
+- **License:** Apache 2.0
+
+## Architecture
+
+| Setting | Value |
+| --- | ---: |
+| Layers | 6 |
+| Hidden size | 256 |
+| Attention heads | 8 |
+| KV heads | 8 |
+| Intermediate size | 768 |
+| Context length | 512 tokens |
+| Vocabulary size | 32,000 |
+| Parameters | ~21.5M |
+| Precision | float32 checkpoint |
+
+## Intended Use
+
+MiniBananaMind-V1 is suitable for:
+
+- Small-scale language-model experiments
+- Educational demos of decoder-only generation
+- Testing tokenization, generation settings, and inference pipelines
+- Research prototypes where a very small causal LM is useful
+
+It is not recommended for production assistants, safety-critical use, or tasks
+that require reliable factual knowledge.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo_id = "BananaMind/MiniBananaMind-V1"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.float32,
+    device_map="auto",
+)
+
+prompt = "A computer is a machine that"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=64,
+        do_sample=True,
+        temperature=0.2,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Generation Notes
+
+Because this is a small base model, output quality depends heavily on prompt
+style and sampling settings. A `temperature` of `0.2` is recommended for more
+stable continuations. For more varied text, increase `temperature` or `top_p`.
+
+## Limitations
+
+- The model may hallucinate facts, names, citations, and dates.
+- It has not been instruction tuned or aligned for chat behavior.
+- It may reproduce biases or unsafe patterns present in web-scale training data.
+- The short 512-token context length limits long-document use.
+- Small model size means weaker reasoning and factual recall than larger LMs.
+
+## Training Data
+
+MiniBananaMind-V1 was trained on streamed FineWeb-Edu text. FineWeb-Edu is a
+large educational-quality web corpus, so users should expect broad web-language
+coverage as well as the usual limitations of internet-scale data.
+
+Training data attribution: this model was trained on
+[FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), a
+dataset released by Hugging Face as part of the FineWeb family.
+
+## Citation
+
+If you use this model in a project, cite the Hugging Face repository and
+attribute the FineWeb-Edu training data:
+
+```bibtex
+@misc{minibananamindv1,
+  title = {MiniBananaMind-V1},
+  author = {BananaMind},
+  year = {2026},
+  howpublished = {\url{https://huggingface.co/BananaMind/MiniBananaMind-V1}}
+}
+```
+
+Dataset: [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)

assets/minibananamind-v1.png

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config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 32,
+  "hidden_act": "silu",
+  "hidden_size": 256,
+  "initializer_range": 0.02,
+  "intermediate_size": 768,
+  "max_position_embeddings": 512,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 6,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.12.1",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.12.1",
+  "use_cache": false
+}

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tokenizer_config.json

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+{
+  "add_prefix_space": null,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}