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jpllm
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Model Details
Model Provider
jpllm
Model Tree
SupraLabs/Supra-Mini-v6-1M
Input Modalities
Output Modalities
Supported Functionality
en)SupraLabs/Supra-Mini-v6-1MNN-oficial is designed for research on micro-scale language models (< 10M parameters), studying parameter-efficient instruction tuning, capacity expansion, and subword vocabulary adaptation. It can generate structured, multi-line instruction completions (e.g., lists, Markdown headers, simple explanations).
This model is not suitable for high-stakes decision-making, medical, legal, or factual lookup tasks due to parameter capacity constraints on world-knowledge storage.
Users should be aware that models under 10M parameters have physical memory bounds for storing factual world knowledge. While NN-oficial maintains strong syntactic coherence, local English grammar, and instruction response formatting, it may hallucinate factual details (e.g., exact geographic capitals or complex mathematical constants).
Use the code below to load and generate text with jpllm/NN-oficial:
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel_id = "jpllm/NN-oficial"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)prompt = ("Below is an instruction that describes a task.\n\n""### Instruction:\nExplain what gravity is in simple terms for a 10-year-old.\n\n""### Response:\n")inputs = tokenizer(prompt, return_tensors="pt")with torch.no_grad():outputs = model.generate(**inputs,max_new_tokens=100,temperature=0.6,top_p=0.9,repetition_penalty=1.2,do_sample=True,pad_token_id=tokenizer.eos_token_id)response = tokenizer.decode(outputs[0], skip_special_tokens=True)if "### Response:\n" in response:print(response.split("### Response:\n")[-1].strip())else:print(response)