A finetuned version of 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:

THINK: Reason about the communication intent
QUANTIZE: Map intent to a semantic anchor in the UCR manifold
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	Merge with base model
Merged 16-bit	slipstream-glm-z1-9b-merged	Direct loading
GGUF Q4_K_M	slipstream-glm-z1-9b-gguf	Ollama / llama.cpp
GGUF Q8_0	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
Training Dataset: hf.co/anthonym21/slipstream-tqt
Paper: Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination
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

anthonym21/slipstream-glm-z1-9b-merged

Slipstream GLM-Z1-9B