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