DeepSeek-V4-Flash-Hybrid-Vision-Reseek

DeepSeek V4 Flash Hybrid Vision Reseek

A vision-enabled assembly of DeepSeek-V4-Flash-0731: the DeepSeek-V4 Flash text model (48 shards, FP8) joined to a MoonViT-style vision tower and multimodal projector, served through vLLM with DSpark speculative decoding.

Built and benchmarked on 2× NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB each, SM120), tensor-parallel 2.

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Benchmarks

All numbers produced with EleutherAI lm-evaluation-harness v0.4.12 against a live vLLM endpoint. Raw result JSON is in benchmarks/. Reproduction commands are in RECIPE.md.

Reasoning and instruction following

Knowledge and commonsense (0-shot, loglikelihood)

Code

Note on HumanEval. The stock lm-eval HumanEval task scores this model at 0.0 — an artifact, not a result. That task expects a raw completion, but a chat endpoint returns prose plus a ```python block, so the built-in filter extracts nothing. The 78.0% above comes from benchmarks/humaneval_chat.py, which pulls the fenced code and runs the official unit tests in a sandboxed subprocess. If you benchmark any chat model on HumanEval, check for this first.

Throughput

Measured on 2× RTX PRO 6000 Blackwell, TP=2, max_num_seqs=16:

Time to first token: 0.085 s median. Prefill: ~8,900 tok/s at 32k tokens. DSpark speculative decoding accepts 3.2–5.9 of 5 draft tokens in practice.

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Vision: architecture, capabilities, limits

The adapter

457M parameters total — roughly 0.1% of the assembled model:

Dimensions chain exactly: the tower emits 1152-wide patch embeddings, a 2×2 merge concatenates them to 4608, and the projector maps that to 4096 — the language model's hidden size. A 448×448 image becomes 256 patches → 64 tokens after merging, which is frugal next to tile-based encoders that spend thousands.

Preprocessing was verified numerically: the normalization LUT produces exactly ±1.0 at grey 0 and 255, with correct 16×16 spatial ordering.

Strengths

• Cheap. ~0.8 GB VRAM, no measurable cost to text quality — all text benchmarks score identically with the vision tower loaded.
• Accurate within its design point. 9/10 on balanced yes/no probes at 448 px; 4/5 on 4-way forced choice with the correct answer listed last. It correctly rejects absent objects (cat, car, people, text, bicycle), so it is not simply answering "yes".
• Token-efficient, per the merge arithmetic above.

Weaknesses

• Fabricates above ~450 px. The dominant failure. Balanced accuracy falls from 6/7 to 4/7 while "yes" answers rise from 4/7 to 6/7 — it drifts toward agreeable confabulation rather than degrading gracefully.
• Weak fine-grained recognition. Category right, identity wrong: a numbat read as "giraffe", a rainbow lightbulb as "camera lens". Scene structure survives; species and object identity do not.
• Blind to synthetic images. Solid colour fills score 1/4 — chance. Expected for a patch-based ViT, since a flat 14×14 patch carries no edges or texture, but it means synthetic images cannot be used to smoke-test the pipeline. A flat test image will look like total failure on a perfectly healthy deployment.
• Thin projector. A 2-layer MLP is the minimum viable bridge from a 1152-wide tower into a 4096-wide LM, and is the most likely cause of the identity confusions above.

Where this adapter could improve

1. Deeper or attention-based projector. Replacing the 2-layer MLP with a cross-attention resampler (Q-Former / Perceiver style) targets the identity errors directly, and at 40M params it is by far the cheapest component to retrain — the tower need not be touched.
2. Higher-resolution alignment training. The >450 px collapse points at the projector rather than the tower: token counts scale correctly with image area, so the encoder handles more patches; it is the mapping that degrades.
3. Negative-example alignment. The failure mode is confident invention, not refusal. The model answers "is there text in this image?" correctly at 448 px, so the capability exists but is not robust to resolution.
4. OCR evaluation. Every fabrication observed involved invented text ("Airbus", "The Great Forest"). This is a hypothesis, not a measurement — it was not tested against an OCR benchmark.

Evaluation gap. The vision figures below come from hand-built probes over roughly ten images, not a standard benchmark. The text scores in this card are lm-eval reproducible; the vision claims are not. Run MMMU, MMBench, or TextVQA before relying on them.

Measured behaviour

What works — images at or below 448 px on the longest side:

What fails:

• Open-ended captioning above ~450 px. The model confabulates. Asked to describe a desert landscape at 896 px it reported "a bird flying over a sunset with the words 'the great outdoors'" — text that does not exist in the image. Feeding a full-resolution photo produced an invented "Airbus" logo.
• Synthetic flat-colour images. Solid fills score at chance (1/4). Flat colour is out of distribution for a patch-based ViT — no edges or texture in any patch. This is expected behaviour, not a defect.

Recommendations:

1. Downscale images to 448 px before sending. Measured effect: one test image went from 5/7 to 7/7 correct. It also cuts a 4736×2656 photo from ~3,100 prompt tokens to ~260 (12× fewer).
2. Ask specific questions ("Is there water in this image?") rather than "describe this image." Factual queries stay accurate where captions drift.

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Quick start

Serve with vLLM

vllm serve /path/to/DeepSeek-V4-Flash-Hybrid-Vision-Reseek \
  --served-model-name dsv4-hybrid-vision \
  --tensor-parallel-size 2 \
  --tokenizer-mode deepseek_v4 \
  --kv-cache-dtype fp8_ds_mla \
  --block-size 256 \
  --max-model-len 262144 \
  --max-num-seqs 16 \
  --gpu-memory-utilization 0.968 \
  --speculative-config '{"method":"dspark","model":"/path/to/model","num_speculative_tokens":5}' \
  --enable-auto-tool-choice \
  --tool-call-parser deepseek_v4

Query it

import base64, io, json, urllib.request
from PIL import Image

URL = "http://127.0.0.1:8000/v1/chat/completions"

def ask(messages, max_tokens=512):
    body = {"model": "dsv4-hybrid-vision", "messages": messages,
            "max_tokens": max_tokens, "temperature": 0.6, "top_p": 0.95}
    req = urllib.request.Request(URL, data=json.dumps(body).encode(),
                                 headers={"Content-Type": "application/json"})
    return json.loads(urllib.request.urlopen(req).read())["choices"][0]["message"]["content"]

# Text
print(ask([{"role": "user", "content": "What port does SSH use?"}]))

# Vision -- downscale to 448 px first (see the vision notes above)
im = Image.open("photo.jpg").convert("RGB")
im.thumbnail((448, 448))
buf = io.BytesIO(); im.save(buf, "PNG")
b64 = base64.b64encode(buf.getvalue()).decode()

print(ask([{"role": "user", "content": [
    {"type": "text", "text": "Is there a mountain in this image? Answer yes or no."},
    {"type": "image_url", "image_url": {"url": "data:image/png;base64," + b64}},
]}]))

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Recommended sampling

The checkpoint's generation_config.json defaults to temperature=1.0, top_p=1.0. At those settings the model pads its answers heavily — asked for a single bash one-liner it returned five variants plus a bullet-point explainer (347 tokens for a one-line question).

These settings were measured to fix that:

{"temperature": 0.6, "top_p": 0.95, "max_tokens": 1024}

Pair them with a concise system prompt for short answers. With both applied, the same one-liner question returns 29 tokens — one command, no menu.

Note that --generation-config vllm makes vLLM ignore the checkpoint's generation_config.json; pass --override-generation-config to set defaults server-side.

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Reasoning mode

Chain-of-thought is off by default — the deepseek_v4 tokenizer sets thinking=False unless a caller opts in with enable_thinking or reasoning_effort. To pin it off server-side regardless of what clients send:

--default-chat-template-kwargs '{"thinking": false, "enable_thinking": false}'

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Known constraints

• KV cache dtype is fixed at fp8_ds_mla on SM120. nvfp4_ds_mla would cut the record from 656 to 432 B/token (~1.52× more cache), and both the CLI and the b12x backend advertise it — but every DeepSeek-V4 attention class reachable on SM120 sets use_fp8_ds_mla_layout = True, which asserts the dtype starts with fp8. Attempting it fails at load: AssertionError: DeepseekV4 fp8_ds_mla layout only supports fp8 kv-cache. The one class with the flag off is gated to compute capability 9/10 (Hopper). The underlying FlashInfer kernel is a prebuilt cubin documented as 584 B/token, BF16 or FP8 E4M3 only, so this needs upstream support — not a config change.
• JIT warmup. vLLM compiles Triton/TileLang kernels on first sight of each tensor shape and logs "causes a latency spike". Measured: 12 such events in the first 14 minutes after boot, each ~6 s against an 0.086 s median. Issue one request per shape class at startup (short/medium/long prompt, vision, tools) to absorb these before real traffic. Compiled kernels persist in the JIT cache.
• Full-context concurrency is 1×. At 262,144 max_model_len the KV cache holds 263,310 tokens — one maximum-length request. Ordinary workloads are unaffected (16k tokens × 4 concurrent measured fine, KV usage under 1%), but simultaneous full-context requests will queue.

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Files

config.json                    model configuration
generation_config.json         default sampling parameters
preprocessor_config.json       vision preprocessing (MoonViT)
tokenizer.json                 tokenizer
tokenizer_config.json          tokenizer configuration
model.safetensors.index.json   weight shard index
provenance.json                component checksums
benchmarks/                    raw lm-eval results + HumanEval script
RECIPE.md                      step-by-step build and evaluation guide

Licence

Inherits the DeepSeek model licence from the base checkpoint. The vision tower and projector derive from their respective upstream sources. Verify licence compatibility for your use case before deploying commercially.

Acknowledgements

• DeepSeek AI — DeepSeek-V4-Flash base model
• vLLM — serving stack, sparse-MLA and DSpark support
• EleutherAI — lm-evaluation-harness