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license: apache-2.0
base_model: zai-org/GLM-Z1-9B-0414
tags:
- slipstream
- multi-agent
- semantic-quantization
- agent-communication
- think-quantize-transmit
- lora
- unsloth
datasets:
- anthonym21/slipstream-tqt
language:
- en
pipeline_tag: text-generation
library_name: peft
---
# Slipstream GLM-Z1-9B
A LORA Adapter for [GLM-Z1-9B-0414](https://huggingface.co/zai-org/GLM-Z1-9B-0414) trained on the **Slipstream protocol** - a semantic quantization system that achieves **82% token reduction** in multi-agent AI communication.
## Model Description
This model has learned the **Think-Quantize-Transmit (TQT)** cognitive pattern:
1. **THINK**: Reason about the communication intent
2. **QUANTIZE**: Map intent to a semantic anchor in the UCR manifold
3. **TRANSMIT**: Output a compact SLIP wire format message
### Example
**Input:**
```
Tell bob to review my authentication code
```
**Output:**
```
THOUGHT: I need bob to do a code review on the auth module
QUANTIZE: [ACTION=request | DOMAIN=task | URGENCY=normal | POLARITY=neutral] -> RequestReview
SLIP: SLIP v1 alice bob RequestReview auth_module
```
## Training Details
| Parameter | Value |
|-----------|-------|
| Base Model | zai-org/GLM-Z1-9B-0414 |
| Method | LoRA (rank=16, alpha=16) |
| Epochs | 2 |
| Learning Rate | 2e-4 |
| Batch Size | 16 (4 × 4 grad accum) |
| Sequence Length | 2048 |
| Training Examples | 2,283 |
| Hardware | Google Colab (A100/V100) |
| Framework | Unsloth + TRL |
### LoRA Target Modules
- Attention: `q_proj`, `k_proj`, `v_proj`, `o_proj`
- MLP: `gate_proj`, `up_proj`, `down_proj`
## Available Formats
| Format | Repository | Use Case |
|--------|------------|----------|
| LoRA Adapter | [slipstream-glm-z1-9b](https://huggingface.co/anthonym21/slipstream-glm-z1-9b) | Merge with base model |
| Merged 16-bit | [slipstream-glm-z1-9b-merged](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-merged) | Direct loading |
| GGUF Q4_K_M | [slipstream-glm-z1-9b-gguf](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-gguf) | Ollama / llama.cpp |
| GGUF Q8_0 | [slipstream-glm-z1-9b-gguf](https://huggingface.co/anthonym21/slipstream-glm-z1-9b-gguf) | Higher quality local |
## Usage
### With Transformers + PEFT
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-Z1-9B-0414")
model = PeftModel.from_pretrained(base_model, "anthonym21/slipstream-glm-z1-9b")
tokenizer = AutoTokenizer.from_pretrained("anthonym21/slipstream-glm-z1-9b")
```
### With Ollama
```bash
# Download GGUF
wget https://huggingface.co/anthonym21/slipstream-glm-z1-9b-gguf/resolve/main/slipstream-q4_k_m.gguf
# Create Modelfile
cat > Modelfile <<EOF
FROM ./slipstream-q4_k_m.gguf
SYSTEM "You are an AI agent using the Slipstream protocol for efficient multi-agent communication."
EOF
# Run
ollama create slipstream -f Modelfile
ollama run slipstream "Tell bob to review my code"
```
### With Unsloth (for inference)
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
"anthonym21/slipstream-glm-z1-9b",
max_seq_length=2048,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
```
## UCR Anchors
The model understands 21 core anchors:
| Category | Anchors |
|----------|---------|
| Requests | `RequestTask`, `RequestReview`, `RequestHelp`, `RequestPlan` |
| Inform | `InformComplete`, `InformProgress`, `InformBlocked`, `InformStatus` |
| Propose | `ProposePlan`, `ProposeChange`, `ProposeAlternative` |
| Evaluate | `EvalApprove`, `EvalReject`, `EvalNeedsWork` |
| Meta | `Accept`, `Reject`, `MetaAck`, `MetaHandoff`, `Fallback` |
## Wire Format
```
SLIP v1 <src> <dst> <anchor> [payload...]
```
Example: `SLIP v1 alice bob RequestReview auth_module`
## Related Resources
- **Project Repo**: [github.com/anthony-maio/slipcore](https://github.com/anthony-maio/slipcore)
- **Training Dataset**: [hf.co/anthonym21/slipstream-tqt](https://huggingface.co/datasets/anthonym21/slipstream-tqt)
- **Paper**: [Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination](https://doi.org/10.5281/zenodo.18063451)
- **PyPI**: `pip install slipcore`
## Citation
```bibtex
@misc{maio2025slipstream,
title={Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination},
author={Maio, Anthony},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/anthonym21/slipstream-glm-z1-9b}
}
```
## License
Apache 2.0
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