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Model: derprofi2431/Prisma-32B
Source: Original Platform

README.md

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