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BananaMind
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Model Details
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BananaMind
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Output Modalities
Supported Functionality
| 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 |
MiniBananaMind-V1 is suitable for:
It is not recommended for production assistants, safety-critical use, or tasks that require reliable factual knowledge.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))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.
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, a dataset released by Hugging Face as part of the FineWeb family.
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
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))@misc{minibananamindv1, title = {MiniBananaMind-V1}, author = {BananaMind}, year = {2026}, howpublished = {\url{https://huggingface.co/BananaMind/MiniBananaMind-V1}}}