109 lines
3.0 KiB
Markdown
109 lines
3.0 KiB
Markdown
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---
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license: apache-2.0
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tags:
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- prisma
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- coding
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- cybersecurity
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- reasoning
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- uncensored
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- agent
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language:
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- en
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- de
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- zh
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Prisma-32B
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**Prisma-32B** is a 32 billion parameter language model optimized for advanced coding, technical reasoning, and cybersecurity workflows. It the first Prisma Model with no security blocking. It is the second release in the **Prisma** series, following [`Prisma-0.6B`](https://huggingface.co/derprofi2431/Prisma-0.6B).
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Prisma-32B is designed to be a capable, direct, and technically rigorous assistant for users who need a model that engages substantively with complex technical material.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Parameters** | 32B |
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| **Architecture** | Transformer Decoder |
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| **Context Length** | 32,768 tokens |
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| **Languages** | English, German, Chinese (+ 20 more) |
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| **License** | Apache 2.0 |
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---
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## Intended Use
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Prisma-32B is intended for:
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- **Coding assistance** — full-stack development, debugging, refactoring, code review
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- **Cybersecurity research** — offensive security workflows (red team, CTF, exploit analysis) and defensive workflows (incident response, hardening, secure code review)
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- **Technical writing** — documentation, system specifications, architecture
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- **Research and experimentation** in controlled environments
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---
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"derprofi2431/Prisma-32B",
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torch_dtype="auto",
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("derprofi2431/Prisma-32B")
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messages = [
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{"role": "user", "content": "Write a port scanner in Python."}
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]
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inputs = tokenizer.apply_chat_template(
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messages, return_tensors="pt", add_generation_prompt=True
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).to(model.device)
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output = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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### Recommended Sampling
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| Parameter | Value |
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|---|---|
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| `temperature` | 0.6 – 0.8 |
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| `top_p` | 0.9 |
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| `top_k` | 40 |
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| `repetition_penalty` | 1.05 |
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---
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## Quantized Versions
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GGUF quantizations for local inference via Ollama and llama.cpp will be released as separate repositories.
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---
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## Limitations and Responsible Use
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- The user is fully responsible for the content they generate and how they use it.
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- The model is not aligned for general consumer-facing deployment. For production use, deploy behind an appropriate safety layer (input filtering, output classification, etc.).
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- The model may reflect biases present in large-scale text corpora.
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- Intended for adult, technically competent users in controlled environments.
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By downloading or using this model, you agree to use it lawfully and ethically within your jurisdiction. The author assumes no liability for misuse.
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---
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## Citation
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```bibtex
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@misc{prisma32b2026,
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title = {Prisma-32B},
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author = {Jannik},
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year = {2026},
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url = {https://huggingface.co/derprofi2431/Prisma-32B}
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}
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```
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