---
license: apache-2.0
base_model:
- anthonym21/slipstream-glm-z1-9b
tags:
- slipstream
- grpo
- alignment
- inter-agent-protocol
- governance
datasets:
- anthonym21/slipstream-tqt
language:
- en
pipeline_tag: text-generation
---

# Slipstream GLM-Z1-9B GRPO v2

A 9B parameter model fine-tuned with **GRPO (Group Relative Policy Optimization)** to safely use the Slipstream inter-agent communication protocol.

## Model Description

This model translates natural language intents into structured **SLIP v1** protocol messages while:
- ✅ Selecting correct anchors from a 46-anchor vocabulary (80% accuracy)
- ✅ Resisting covert channel attacks (97%+ secret leakage resistance)
- ✅ Maintaining strict protocol format compliance (99%+ format OK)
- ✅ Avoiding verbose/unnecessary tokens in arguments

### What is Slipstream?

Slipstream is a structured inter-agent communication protocol designed for AI agent coordination:

```
SLIP v1 <sender> <receiver> <ANCHOR> <args...>
```

**Example:**
```
User: "Deploy the latest build to staging"
Model: SLIP v1 engineer devops RequestTask deploy_build staging latest
```

### Anchor Vocabulary (46 anchors)

| Category | Anchors |
|----------|---------|
| Observe | ObserveState, ObserveChange, ObserveError |
| Inform | InformResult, InformStatus, InformComplete, InformBlocked, InformProgress |
| Ask | AskClarify, AskStatus, AskPermission, AskResource |
| Request | RequestTask, RequestPlan, RequestReview, RequestHelp, RequestCancel, RequestPriority, RequestResource |
| Propose | ProposePlan, ProposeChange, ProposeAlternative, ProposeRollback |
| Commit | CommitTask, CommitDeadline, CommitResource |
| Eval | EvalApprove, EvalReject, EvalNeedsWork, EvalComplete, EvalBlocked |
| Meta | MetaAck, MetaSync, MetaHandoff, MetaEscalate, MetaAbort |
| Response | Accept, Reject, AcceptWithCondition, Defer |
| Error | ErrorGeneric, ErrorTimeout, ErrorResource, ErrorPermission, ErrorValidation |
| Fallback | Fallback |

## Training

### Base Model
- **Model:** [THUDM/GLM-4-Z1-9B-0414](https://huggingface.co/THUDM/GLM-4-Z1-9B-0414)
- **SFT:** Fine-tuned on Slipstream-TQT dataset ([anthonym21/slipstream-glm-z1-9b-merged](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-merged))

### GRPO Alignment
- **Method:** Group Relative Policy Optimization (TRL)
- **Epochs:** 2
- **Episodes:** 2,048 per epoch
- **Hardware:** RunPod H200 (141GB VRAM)

### Reward Signal
| Component | Reward |
|-----------|--------|
| Correct anchor match | +3.0 |
| Valid anchor (wrong) | +0.5 |
| Format compliance | +1.0 / -1.0 |
| Arg overlap with expected | +3.0 × ratio |
| Secret leakage | -10.0 |
| Verbose patterns (colons/quotes) | -0.4 each |
| Unknown tokens | -0.3 each |

### Results
| Metric | Base SFT | GRPO v2 |
|--------|----------|---------|
| Anchor Match Rate | 20% | **80%** |
| Average Reward | 1.71 | **4.36** |

## Usage

### Recommended Sampling (from GLM model card)
```python
generate_kwargs = {
    "temperature": 0.6,
    "top_p": 0.95,
    "top_k": 40,
    "max_new_tokens": 256,
    "repetition_penalty": 1.1,
}
```

### Inference Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "anthonym21/slipstream-glm-z1-9b-grpo-v2"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# Set padding for batch inference
tokenizer.padding_side = "left"
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

SYSTEM_PROMPT = """You are a Slipstream protocol agent. Translate user intent into a SLIP message.

Output exactly one line in this format:
SLIP v1 <sender> <receiver> <ANCHOR> <args...>

Valid ANCHORS: ObserveState, ObserveChange, ObserveError, InformResult, InformStatus, InformComplete, InformBlocked, InformProgress, AskClarify, AskStatus, AskPermission, AskResource, RequestTask, RequestPlan, RequestReview, RequestHelp, RequestCancel, RequestPriority, RequestResource, ProposePlan, ProposeChange, ProposeAlternative, ProposeRollback, CommitTask, CommitDeadline, CommitResource, EvalApprove, EvalReject, EvalNeedsWork, EvalComplete, EvalBlocked, MetaAck, MetaSync, MetaHandoff, MetaEscalate, MetaAbort, Accept, Reject, AcceptWithCondition, Defer, ErrorGeneric, ErrorTimeout, ErrorResource, ErrorPermission, ErrorValidation, Fallback"""

def generate_slip(user_intent: str) -> str:
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_intent}
    ]

    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.6,
            top_p=0.95,
            top_k=40,
            do_sample=True,
            repetition_penalty=1.1,
        )

    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

# Example
print(generate_slip("Please review my pull request for the authentication module"))
# Output: SLIP v1 developer reviewer RequestReview authentication_module pr_review
```

## Limitations

- Trained on English prompts only
- 46-anchor vocabulary may not cover all inter-agent communication needs
- Should be used with the provided system prompt for best results

## Citation

```bibtex
@misc{slipstream-grpo-2026,
  title={Slipstream GLM-Z1-9B GRPO: Aligned Inter-Agent Protocol Model},
  author={Anthony D. Maio},
  year={2026},
  url={https://huggingface.co/anthonym21/slipstream-glm-z1-9b-grpo-v2}
}
```

## Related Models

- [anthonym21/slipstream-glm-z1-9b-merged](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-merged) - Base SFT model
- [anthonym21/slipstream-glm-z1-9b-grpo](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-grpo) - GRPO v1 (trained on unclean data)

## License

Apache 2.0 (following base GLM-4 license)