Instructions to use cristianoaredes/orqo-hype-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use cristianoaredes/orqo-hype-v1 with PEFT:
```
Task type is invalid.
```

How to use cristianoaredes/orqo-hype-v1 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("cristianoaredes/orqo-hype-v1")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cristianoaredes/orqo-hype-v1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cristianoaredes/orqo-hype-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cristianoaredes/orqo-hype-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cristianoaredes/orqo-hype-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cristianoaredes/orqo-hype-v1:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cristianoaredes/orqo-hype-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cristianoaredes/orqo-hype-v1:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cristianoaredes/orqo-hype-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cristianoaredes/orqo-hype-v1:Q4_K_M

Use Docker

docker model run hf.co/cristianoaredes/orqo-hype-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use cristianoaredes/orqo-hype-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cristianoaredes/orqo-hype-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cristianoaredes/orqo-hype-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cristianoaredes/orqo-hype-v1:Q4_K_M

Ollama
How to use cristianoaredes/orqo-hype-v1 with Ollama:
```
ollama run hf.co/cristianoaredes/orqo-hype-v1:Q4_K_M
```
Unsloth Desktop

How to use cristianoaredes/orqo-hype-v1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cristianoaredes/orqo-hype-v1"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cristianoaredes/orqo-hype-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use cristianoaredes/orqo-hype-v1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "cristianoaredes/orqo-hype-v1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "cristianoaredes/orqo-hype-v1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "cristianoaredes/orqo-hype-v1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use cristianoaredes/orqo-hype-v1 with Docker Model Runner:
```
docker model run hf.co/cristianoaredes/orqo-hype-v1:Q4_K_M
```

Lemonade

How to use cristianoaredes/orqo-hype-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cristianoaredes/orqo-hype-v1:Q4_K_M

Run and chat with the model

lemonade run user.orqo-hype-v1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cristianoaredes/orqo-hype-v1 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cristianoaredes/orqo-hype-v1"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default cristianoaredes/orqo-hype-v1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cristianoaredes/orqo-hype-v1 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "cristianoaredes/orqo-hype-v1"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "cristianoaredes/orqo-hype-v1" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Orqo Hype v1

Orqo Hype is a specialist composition model for the HyperFrames stack. Given a creative brief, it produces a staged pipeline:

design JSON → storyboard → scene HTML / GSAP

suitable for HyperFrames assemble → lint → render (MP4).


Product	Orqo Hype
Version	v1
HF id	`cristianoaredes/orqo-hype-v1`
Visibility	Public
Internal train id	`sft-v9b-mix`
Load base (compatibility only)	`Qwen/Qwen2.5-Coder-7B-Instruct`

Branding: the product name is Orqo Hype. The foundation model is listed only for PEFT / merge load compatibility — it is not part of the product name.

Verified repository contents

Live Hub inventory verified on 2026-07-17:

Path	Format	Intended use
`lora/`	PEFT adapter	Load with the compatible base model
`merged/`	Merged Hugging Face weights (4 safetensors shards)	Local Transformers-compatible inference without PEFT
`gguf/`	`Q4_K_M` GGUF	Local llama.cpp-compatible inference
`mlx-4bit/`	MLX 4-bit, group size 64, 4.501 bits/weight	Local Apple Silicon inference with `mlx-lm`
`coreml/`	Core ML 4-bit palettized `.mlpackage`	iOS 18+/macOS 15+ compatibility research; smoke-tested on macOS only

orqo-hype-v1/
├── README.md                 ← this card
├── lora/                     # PEFT adapter + tokenizer + train_metrics
├── merged/                   # full HF weights
├── gguf/                     # Q4_K_M GGUF + metadata
├── mlx-4bit/                 # MLX 4-bit weights + export metadata
└── coreml/                   # Core ML package + export metadata

Evidence: live Hugging Face Hub inventory plus mlx-4bit/export_metadata.json and coreml/export_metadata.json in this repository. The 2026-07-12 packaging record covers lora/, merged/, and gguf/; the live 2026-07-17 inventory additionally verifies MLX and Core ML.

Local usage

Cost boundary: this card does not advertise or invoke a hosted inference endpoint. The examples below download the public model artifacts and run them on hardware controlled by the user. They do not call a project-owned paid inference service.

1) Load LoRA (PEFT)

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE = "Qwen/Qwen2.5-Coder-7B-Instruct"  # load compatibility only
REPO = "cristianoaredes/orqo-hype-v1"

tok = AutoTokenizer.from_pretrained(BASE)
model = AutoModelForCausalLM.from_pretrained(
    BASE, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, REPO, subfolder="lora")
model.eval()

2) Load merged (no PEFT)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

REPO = "cristianoaredes/orqo-hype-v1"
tok = AutoTokenizer.from_pretrained(REPO, subfolder="merged")
model = AutoModelForCausalLM.from_pretrained(
    REPO,
    subfolder="merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

3) GGUF (llama.cpp)

hf download cristianoaredes/orqo-hype-v1 \
  --include "gguf/*Q4_K_M.gguf" --local-dir ./orqo-hype-v1

./llama-cli -m ./orqo-hype-v1/gguf/sft-v9b-mix-Q4_K_M.gguf -p "5s logo sting..."

4) MLX (Apple Silicon)

hf download cristianoaredes/orqo-hype-v1 \
  --include "mlx-4bit/*" --local-dir ./orqo-hype-v1

python -m mlx_lm generate --model ./orqo-hype-v1/mlx-4bit \
  --prompt "5s logo sting navy gold fintech" --max-tokens 512

Verified export metadata: 4-bit quantization, group size 64, 4.501 bits/weight, converted on Apple Silicon.

Local benchmark recorded on 2026-07-17 with a local copy of the published MLX export, an Apple M5 Pro with 24 GB unified memory, mlx-lm 0.31.3, prompt length 512, 64 generated tokens and two measured trials after warmup:

Batch	Prompt tok/s (aggregate)	Generation tok/s (aggregate)	Peak memory (GB)
1	567.758	43.856	4.848
2	777.954	63.143	5.105
4	864.624	75.811	5.114
8	952.250	86.278	5.546

Method: the official synthetic python -m mlx_lm.benchmark runner. These are local throughput measurements for this exact configuration, not latency or capacity guarantees for another machine, context length or real creative workload. No speculative draft model was used.

5) Core ML

Artifact: coreml/model.mlpackage.
Recipe: static trace with seq_len=512, FP16 followed by 4-bit k-means palettization with group size 16.
Deployment specification: iOS 18 / macOS 15 compatibility; runtime smoke was performed on macOS only.
Limitation recorded by the exporter: no stateful KV cache, full forward pass per token, smoke-grade throughput, quality not benchmarked, and no physical-iOS-device validation.

Intended use

In scope

Short motion-graphics / promo compositions for HyperFrames:
- logo stings, title cards, product teasers
- lower-thirds, reels-style kinetic type
- multi-scene storyboards with GSAP timelines
Staged outputs: design.json → storyboard.json → per-scene HTML

Out of scope

Open-domain chat or general coding assistant
Unfiltered web agent / tool-use
Claiming pure single-shot quality equal to the full delivery pipeline (see Evaluation)

Recommended integration path

Staged generation (design → storyboard → scenes) — system prompts A/B/C
Optional deterministic post-decode assists (font inject, structure guards, motion-craft boost) when shipping quality floors
hyperframes assemble → lint
Rendering infrastructure is integrator-controlled and outside this model card's scope

Raw single-shot model quality ≠ full delivery-path metrics.

Prompting (staged)

The generation workflow uses three stages (compact form):

Stage	Role	Output
A	Art director	`design.json` — format, palette, typography, motion, style
B	Scene director	`storyboard.json` — scenes with duration + beat
C	Motion designer	Full HTML scene: GSAP CDN, `data-composition-*`, `window.__timelines`

Example brief:

8s product teaser: AI video editor, bold kinetic type, dark studio, navy and electric cyan

Versioned integration contract

The reference integration uses a single non-destructive tool named generate_hyperframe_composition. It accepts a bounded brief, target format, maximum scene count, temperature and output budget, then returns design, storyboard and structurally validated scene HTML under schema version 1.0.0; downstream HyperFrames lint is still required.

Input	Contract
`brief`	3–4000 characters
`format`	`16:9`, `9:16` or `1:1`
`max_scenes`	integer from 1 to 8
`temperature`	number from 0 to 1.5
`max_output_tokens`	integer from 256 to 4096

This is an integration contract, not a hosted tool or endpoint. Backend URLs and credentials are operator configuration and are not accepted as tool arguments.

Training summary

Item	Value
Method	SFT adapter (`sft-v9b-mix`)
Train records	2150 (`records_v9b_train`, recipe-3 mix)
Epochs	2
Max sequence	8192
Learning rate	2e-4
Steps	538
Train loss	0.3317
Training hardware	NVIDIA A100-80GB

Subsequent experiments (motion DPO, atomic DPO, residual curriculum SFT) were not promoted over this champion.

Evidence: .archagents/13-execution/runs/RUN-20260711-des0012-e2e.md.

Evaluation (honest)

HyperFrames golden set n=72, Orqo delivery stack around this adapter (2026-07-11):

Gate	Result	Note
Lint (guarded delivery path)	72/72 (100%)	Deterministic post-decode guards included
Motion craft mean (uplifted delivery path)	7.0972	Temporal VLM evaluation on rendered MP4s
Diversity-out (student fonts)	soft PASS	Entropy 3.8629 after the font-pool assist
Raw single-shot lint	66/72 (91.7%)	Champion adapter without claiming the guarded pipeline as raw model quality

Evidence:

.archagents/14-verify/reports/VER-served-sft-v9b-mix.json
.archagents/14-verify/reports/VER-20260711-motion-craft-sft-v9b-mix-gate7.json
.archagents/14-verify/reports/VER-20260711-diversity-out-sft-v9b-mix.md
.archagents/14-verify/reports/VER-20260711-des0012-u61.md

Limitations

Specialist model — weak outside HyperFrames composition briefs.
Storyboard may emit source.type=block; those scenes are resolved from the deterministic allowlisted block catalogue and do not trigger a Stage C HTML request.
Quality floors assume the delivery pipeline, not bare chat alone.
MLX and GGUF quantisation may change creative output relative to merged weights.
The Core ML export is smoke-grade: static sequence length, no stateful KV cache, no recorded quality benchmark, and no physical iOS device proof.
No hosted endpoint, uptime commitment, or managed inference service is documented by this card.

License & attribution

This packaging / adapter card: Apache-2.0
Base model: follow Qwen/Qwen2.5-Coder-7B-Instruct terms
HyperFrames / Orqo branding remain with their respective owners

Citation

@misc{orqo-hype-v1,
  title        = {Orqo Hype v1: HyperFrames Composition Specialist},
  author       = {Aredes, Cristiano},
  year         = {2026},
  howpublished = {Hugging Face model card},
  note         = {Public package: LoRA, merged weights, GGUF, MLX 4-bit, and Core ML},
  url          = {https://huggingface.co/cristianoaredes/orqo-hype-v1}
}

Changelog

Version	Date	Notes
orqo-hype-v1	2026-07-11	Branded packaging of champion `sft-v9b-mix` LoRA
orqo-hype-v1	2026-07-12	Initial package: `lora/` · `merged/` · `gguf/`
orqo-hype-v1	2026-07-17	Public inventory verified with MLX/Core ML metadata; hosted-serving references removed

Downloads last month: 12

MLX

Hardware compatibility

Quantized

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for cristianoaredes/orqo-hype-v1

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

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(753)

this model