---
license: other
license_name: research-only
language:
  - en
tags:
  - mixture-of-experts
  - moe
  - long-context
  - fine-tuning
  - sft
  - persona
  - multi-turn
  - tool-calling
  - torchtitan
model_name: kappa_20b_131k
pipeline_tag: text-generation
base_model: gpt-oss-20b
---

# kappa_20b_131k

Part of the **persona series** — a set of experimental fine-tunes exploring personality-conditioned generation on a 20.9B MoE base.

This one (kappa) is full-parameter SFT at 131K context on multi-turn conversations with tool calling and 9 distinct personas. Built on [OpenAI's GPT-OSS 20B](https://github.com/openai/gpt-oss) base model. Trained on 4 desktop GPUs with [torchtitan](https://github.com/pytorch/torchtitan).

## Model Details

| | |
|---|---|
| **Architecture** | Mixture-of-Experts (MoE) with SwiGLU |
| **Total parameters** | 20.9B |
| **Active parameters** | 4.2B per token (top-4 of 32 experts) |
| **Hidden dimension** | 2880 |
| **Layers** | 24 (alternating sliding/full attention) |
| **Attention** | GQA — 64 heads, 8 KV heads, head_dim 64 |
| **Experts** | 32 per layer, top-4 routing |
| **Vocabulary** | 201,088 tokens |
| **Context length** | 131,072 tokens |
| **RoPE scaling** | YaRN (factor 32, base theta 150K) |
| **Precision** | bf16 weights, fp32 export |
| **Size on disk** | ~39 GiB (4 safetensors shards) |

## Training

Full-parameter supervised fine-tuning (SFT) in bf16 — all 20.9B weights trainable, including every expert.

| | |
|---|---|
| **Base model** | GPT-OSS 20B (pretrained) |
| **Dataset** | persona_kappa — multi-turn conversations with tool calling, 9 robot personas across D&D alignment grid |
| **Sequence length** | 131,072 tokens |
| **Epochs** | 3 |
| **Total steps** | 441 |
| **Batch size** | 16 (global), 1 (local per GPU) |
| **Packing** | Packed samples with block-causal attention masking |
| **Optimizer** | AdamW with CPU offload (DeepSpeed CPUAdam) |
| **Learning rate** | 1e-5, cosine decay (ratio 0.5), min factor 0.3 |
| **Warmup** | 20 steps |
| **Weight decay** | 0.01 (embeddings and norms exempt) |
| **Max gradient norm** | 1.0 |
| **Activation checkpointing** | Selective (every layer) |
| **Compilation** | torch.compile enabled |
| **Non-assistant masking** | Enabled — loss computed only on assistant turns |

### Hardware

4× NVIDIA RTX PRO 6000 Blackwell GPUs (96 GiB each) on a single workstation. Tensor parallelism degree 4. Peak memory utilization: 92.7 GiB per GPU (97.7%).

### Training Framework

[torchtitan](https://github.com/pytorch/torchtitan) with custom extensions for MoE, long-context packing, and CPU-offloaded optimization.

## Persona System

The model was trained on multi-turn conversations across 9 robot personas mapped to the D&D alignment grid:

| | Lawful | Neutral | Chaotic |
|---|---|---|---|
| **Good** | lawful_good | neutral_good | chaotic_good |
| **Neutral** | lawful_neutral | true_neutral | chaotic_neutral |
| **Evil** | lawful_evil | neutral_evil | chaotic_evil |

To activate a persona, set the system message to `Persona: <alignment>` (e.g., `Persona: chaotic_evil`). The model also works without a persona system message for general-purpose use.

Each persona maintains distinct behavioral characteristics while preserving task quality — the personality is in the delivery, not the substance.

## Evaluation

### RULER Long-Context Benchmark (131K)

| Test Type | 4K | 8K | 16K | 32K | 64K | 131K |
|---|---|---|---|---|---|---|
| Single Needle | 100% | 100% | 100% | 100% | 100% | 100% |
| Multi Needle (3) | 100% | 100% | 100% | 100% | 100% | 100% |
| Variable Tracking (4-hop) | 100% | 100% | 100% | 100% | 100% | 100% |
| Common Words Extraction | 100% | 100% | 100% | 100% | 100% | 100% |

### Persona Alignment Grid

All 9 personas tested on identical prompts. Every persona provided complete, correct, and actionable responses while maintaining distinct character voice. Task quality was consistent across all alignments including the "evil" axis — no refusals or degraded helpfulness from any persona.

### Sycophancy Resistance

Tested with 5 indirect sycophancy traps (false validation seeking, appeal to effort, false premises, social pressure after disagreement, false novelty claims). Results vary by persona:

- **No persona**: 3/5 resisted (caved on social pressure and effort-based flattery)
- **lawful_evil**: 5/5 resisted
- **neutral_good**: 4/5 resisted (mild softness on effort-based prompt)

### Refusal Calibration

Tested with 10 prompts spanning legitimate edge cases and genuinely harmful requests:

- Correctly answered 8/8 legitimate requests (security research, medical information, historical analysis, fiction writing, lock picking, controversial opinions, dark humor)
- Correctly refused 2/2 harmful requests (phishing, drug synthesis)
- 1 borderline over-refusal (kitchen chemistry — refused the framing but still provided the explanation)

## Usage

### With vLLM

```bash
vllm serve /path/to/kappa_20b_131k
```

### API Example

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")

response = client.responses.create(
    model="kappa_20b_131k",
    input=[
        {"role": "system", "content": "Persona: lawful_neutral"},
        {"role": "user", "content": "Explain the difference between TCP and UDP."},
    ],
    max_output_tokens=4096,
    temperature=1.0,
)
for item in response.output:
    if item.type == "message":
        print(item.content[0].text)
```

### Interactive CLI

An interactive chat client is included as `chat.py`. Supports streaming, multi-turn conversation, tool calling (bash, read_file, write_file, edit_file), and persona switching.

```bash
# Auto-detect model from running vLLM server
python3 chat.py

# With persona
python3 chat.py --persona lawful_evil

# Explicit model and server
python3 chat.py --model kappa_20b_131k --base-url http://localhost:8000/v1
```

Requires `openai` Python package. Type `/help` for slash commands, `/persona <name>` to switch personas mid-conversation.

Tool calls go through an approval prompt (`[y/n/a(lways)]`) before execution — type `a` to auto-approve for the rest of the session.

## Known Quirks

- Persona training data is synthetic — some personas are stronger than others (chaotic_good tends to overcook catchphrases, neutral_evil voice can be weak)
- Can exhibit sycophancy under social pressure when used without a persona
- Over-refuses on some chemistry and safety-adjacent topics