IMPLEMENTATION_REPORT.md

Phillnet Mini Text-Vision

Build status: Complete and validated on 21 August 2026.

This package is a deliberately lean derivative of ayjays132/Phillnet-Mini-Omni-Max. It retains text generation and still-image understanding only. The upstream model card identifies the checkpoint as a custom-code Transformers model and describes a separate SDXL image-generation route; this build removes that route completely rather than merely disabling it at runtime. 1

Scope lock: This package supports only language generation and visual question answering over still images. It contains no SDXL U-Net, VAE, SDXL text encoders, diffusion scheduler, text-to-image API, text-to-video API, tool runtime, agent runtime, audio endpoint, or video endpoint.

What was downloaded and what was removed

The full original Hugging Face snapshot was mirrored before transformation. Its source inventory contained 99 files, including 18 files in the phillnet3_sdxl/ subtree. The final lean package contains 48 files and is 1,785,035,602 bytes on disk, compared with 10,403,517,915 bytes for the complete downloaded snapshot.

Artifact	Original snapshot	Final text-vision package	Result
Total package size	10,403,517,915 bytes	1,785,035,602 bytes	82.84% reduction
SDXL subtree	8,608,200,761 bytes across 18 files	0 bytes	Removed
Core model.safetensors	1,763,655,304 bytes	1,763,655,304 bytes	Retained byte-for-byte
Package files	300 files in downloaded snapshot	48 files	Runtime surface reduced

The retained core checkpoint has SHA-256:

f1a913f99f8ce921c1aa79982a09eaee6744448766f09a4756751e2d4b9342fc

That hash is identical before and after the stripping process. Therefore, the language and visual-encoder weights that reside in model.safetensors were not rewritten or quantized.

Exact removal policy

The SDXL assets were not left as optional downloads. They were physically excluded from the final package together with the loading paths that referenced them. This prevents the deployment from downloading, initializing, or accidentally exposing a diffusion backend.

Removed area	Files or behavior removed	Why it is absent
SDXL checkpoint	phillnet3_sdxl/, including two phillnet-image-*.safetensors shards	These shards contain the diffusion donor banks and were the primary storage cost.
SDXL runtime	phillnet3_sdxl.py, synthesis.py, phillnet3_bridge.py, generation_acceleration.py	They create or accelerate the SDXL U-Net, VAE, CLIP encoders, scheduler, and image/video synthesis routes.
Generation APIs	generate_image, generate_image_prompt, generate_video, generate_video_prompt, synthesis_losses	These methods were removed from DendroForCausalLM, not merely hidden.
Diffusion configuration	SDXL/diffusion-specific fields and validation from config.json and configuration_dendro_omni.py	A lean model should not advertise, validate, or accept inactive synthesis configuration.
Non-core runtimes	Agent, tool, orchestration, Smolagents, legacy showcase, cache, and image-generation files	They are outside the requested text-and-vision serving surface.
External processor declaration	Stale Qwen3VLProcessor declaration	Replaced by the bundled DendroVisionProcessor to make image input local and self-contained.

The remaining configuration declares model_capabilities: ["text-generation", "image-understanding"] and text_vision_only: true.

What remains and why it works

The central retained checkpoint uses a packed single-source SafeTensors layout. It has one top-level tensor rather than many separately named tensors, while transplant_manifest.json maps portions of that source to language and visual components. The manifest contains 153 retained model.visual.* mappings. This is distinct from the deleted SDXL subtree: those mappings implement visual encoding for image understanding, whereas SDXL supplied image synthesis.

The new processing_dendro_omni.py is the key compatibility layer. It loads text and still images locally, normalizes an image, duplicates a still frame to satisfy the visual tower’s temporal patch dimension, constructs visual patches, inserts exactly the matching number of image placeholder tokens, and emits the pixel_values, image_grid_thw, and mm_token_type_ids tensors needed by the retained visual transformer. The processor is registered in both config.json and preprocessor_config.json, so AutoProcessor.from_pretrained(..., trust_remote_code=True) resolves to DendroVisionProcessor.

Supported path	Status	Implementation
Text completion	Enabled	model.generate(...) with reasoning_effort="direct" for low-latency serving.
Text forward pass	Enabled	Standard DendroForCausalLM.forward(...).
Still-image understanding	Enabled	DendroVisionProcessor plus model.answer_image(...) or direct multimodal model calls.
SDXL image generation	Removed	No weights, no module, no API, no dependency.
Video generation	Removed	No API, no diffusion route, no endpoint.
Audio / tools / agents	Not exposed	Excluded from the lean HTTP service and removed where they were optional runtime layers.

Verification evidence

Verification was intentionally separated into structural, load, forward, and serving checks. The checks prove that the package can load and execute the retained text and visual-input paths. They do not constitute a semantic-quality benchmark; output quality must be evaluated separately on representative user tasks.

Check	Result	Evidence
Python syntax compilation	Passed	Lean runtime modules, processor, configuration, model, and service compiled.
SDXL filesystem scan	Passed	No SDXL-named source or weight artifact remains in the final package.
Synthesis-code scan	Passed	No diffusers, StableDiffusion, DendroSharedDiffusion, image/video-generation method, or SDXL adapter reference remains in Python runtime code.
Configuration deserialization	Passed	DendroOmniConfig.from_pretrained(...) loaded from the stripped directory.
Processor discovery	Passed	AutoProcessor resolves to DendroVisionProcessor.
Model loading	Passed	AutoModelForCausalLM instantiated DendroForCausalLM in BF16 on CPU.
Text forward pass	Passed	Finite logits with shape [1, 27, 248320].
Image forward pass	Passed	Finite logits with shape [1, 61, 248320].
Visual-input contract	Passed	One 32×32 test image produced one visual placeholder and four visual patches.
Direct text generation	Passed	Two output tokens completed in 2.224 seconds in the sandbox CPU test.
Local deployment health	Passed	/health returns exactly the two enabled capabilities and the disabled set.
Local text endpoint	Passed	/v1/chat/completions completed a direct-mode text request.
Local image endpoint	Passed	/v1/chat/completions accepted a valid base64 still image plus text question.
Public temporary health check	Passed	Temporary proxied endpoint returned the expected health payload.

Local use

Install the model dependencies from this directory, then load it using the local custom-code implementation:

cd Phillnet-Mini-Text-Vision
pip install -r requirements.txt

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

model_dir = "./Phillnet-Mini-Text-Vision"
processor = AutoProcessor.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_dir,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
).eval()

encoded = processor("Reply with one word: blue", return_tensors="pt")
output = model.generate(
    **encoded,
    max_new_tokens=32,
    reasoning_effort="direct",
    do_sample=False,
)
print(processor.tokenizer.decode(output[0], skip_special_tokens=True))

For still-image questions, pass OpenAI-style multimodal message content to the processor:

from PIL import Image

encoded = processor.apply_chat_template(
    [{
        "role": "user",
        "content": [
            {"type": "image", "image": Image.open("example.png").convert("RGB")},
            {"type": "text", "text": "Describe the visible objects."},
        ],
    }],
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)

answer = model.generate(
    **encoded,
    max_new_tokens=128,
    reasoning_effort="direct",
    do_sample=False,
)
print(processor.tokenizer.decode(answer[0], skip_special_tokens=True))

Deployment

The package contains a minimal FastAPI service in server.py and a Dockerfile. The service exposes only /health and /v1/chat/completions.

Run directly

cd Phillnet-Mini-Text-Vision
pip install -r requirements.txt -r requirements-server.txt
uvicorn server:app --host 0.0.0.0 --port 8000

Run in a container

docker build -t phillnet-mini-text-vision .
docker run --rm -p 8000:8000 phillnet-mini-text-vision

The package was verified on CPU using BF16 weights. Production capacity must account for the 1.76 GB checkpoint plus framework and request-memory overhead; test the target hardware under representative image sizes and concurrent-request volume before enabling unrestricted traffic.

Health check

curl http://localhost:8000/health

Expected response:

{
  "status": "ok",
  "capabilities": ["text-generation", "image-understanding"],
  "disabled": ["image-generation", "video-generation", "audio", "tools", "agents"]
}

Text request

curl http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  --data '{
    "messages": [{"role": "user", "content": "Give a one-sentence summary of caching."}],
    "max_tokens": 96,
    "reasoning_effort": "direct"
  }'

Still-image question

The endpoint accepts image_base64; remote image URLs are intentionally not fetched, avoiding a server-side request-forgery surface.

IMAGE_B64=$(base64 -w0 example.png)
curl http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  --data "{
    \"messages\": [{
      \"role\": \"user\",
      \"content\": [
        {\"type\": \"image\", \"image_base64\": \"${IMAGE_B64}\"},
        {\"type\": \"text\", \"text\": \"What is visible in this image?\"}
      ]
    }],
    \"max_tokens\": 128,
    \"reasoning_effort\": \"direct\"
  }"

Temporary deployment

A verified temporary endpoint is currently available at:

https://8000-iq1jte46c8ls1hzprn1yn-9c029be5.us5.manus.computer

Its health endpoint is:

https://8000-iq1jte46c8ls1hzprn1yn-9c029be5.us5.manus.computer/health

This URL is for validation only. It is backed by the session sandbox and will not provide durable production hosting. For a persistent deployment, use the included container definition on a host with enough RAM for the retained BF16 checkpoint and configure authentication, TLS termination, logging, rate limits, and request-size limits at the deployment boundary.

Reproducibility artifacts

The parent workspace contains the following non-runtime artifacts:

File	Purpose
build_text_vision_only.py	Rebuilds the lean package from the fully downloaded original snapshot without executing remote model code.
validate_text_vision_package.py	Structural, configuration, processor, and checkpoint-inventory validation.
load_text_vision_model.py	Controlled model-load verification.
run_text_vision_forward.py	Text, image, and direct-generation forward smoke test.
original_weight_sha256.txt	Checksums captured from the complete original snapshot.
SOURCE_INVENTORY.md	Source repository inventory.

References