Instructions to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B

SGLang

How to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/Jackrong/DeepSeek-V4-Pro-Qwen3.5-9B
```

Jackrong commited on 2 days ago

Commit

737c3b3

0 Parent(s):

Current release snapshot

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+---
+base_model: unsloth/Qwen3.5-9B
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3_5
+- reasoning
+- distillation
+- deepseek
+- sft
+- math
+- stem
+- tool-use
+- function-calling
+- mtp
+license: apache-2.0
+language:
+- en
+- zh
+- ko
+- ja
+- es
+- ru
+pipeline_tag: text-generation
+---
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 16px; box-shadow: 0 10px 15px -3px rgba(0,0,0,0.05), 0 4px 6px -2px rgba(0,0,0,0.05); overflow: hidden; background: #ffffff; margin-bottom: 30px;">
+  <div style="background: linear-gradient(135deg, #7c3aed 0%, #4c1d95 100%); padding: 24px; color: white;">
+    <div style="display: flex; align-items: center; justify-content: space-between; flex-wrap: wrap; gap: 10px;">
+      <h1 style="margin: 0; font-size: 26px; font-weight: 800; color: white; border: none;">🧠 DeepSeek-V4-Pro-Qwen3.5-9B</h1>
+      <span style="background: #10b981; color: white; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; text-transform: uppercase; letter-spacing: 0.5px;">Distillation Release</span>
+    </div>
+    <p style="margin: 8px 0 0 0; font-size: 14px; color: #ddd6fe; font-weight: 500;">A compact 9B reasoning model distilled from DeepSeek-V4-Pro in Max Effect mode</p>
+  </div>
+  <div style="display: flex; gap: 8px; flex-wrap: wrap; padding: 12px 24px; background: #f8fafc; border-bottom: 1px solid #e2e8f0;">
+    <span style="background: #f3e8ff; color: #6b21a8; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #e9d5ff;">🔬 DeepSeek-V4-Pro Distillation</span>
+    <span style="background: #dbeafe; color: #1e40af; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #bfdbfe;">🧠 9B Parameters</span>
+    <span style="background: #e0f2fe; color: #0369a1; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #bae6fd;">📐 ~250K Math &amp; STEM Samples</span>
+    <span style="background: #d1fae5; color: #065f46; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #a7f3d0;">⚡ MTP &amp; NVFP4 Evaluated</span>
+  </div>
+  <div style="padding: 24px; display: flex; flex-direction: column; gap: 20px;">
+    <div style="background: #f5f3ff; border-left: 5px solid #7c3aed; padding: 16px; border-radius: 0 8px 8px 0;">
+      <h3 style="margin: 0 0 8px 0; font-size: 15px; color: #6d28d9; font-weight: 700;">💡 What is DeepSeek-V4-Pro-Qwen3.5-9B?</h3>
+      <p style="margin: 0; font-size: 13px; color: #334155; line-height: 1.7;"><b>DeepSeek-V4-Pro-Qwen3.5-9B</b> is a reasoning-focused fine-tune of <b>Qwen3.5-9B</b>, distilled from responses generated by <b>DeepSeek-V4-Pro in Max Effect mode</b>. The release concentrates its supervised signal on mathematics and STEM problem solving while preserving the efficiency and practical deployability of the 9B parameter class.</p>
+    </div>
+    <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(190px, 1fr)); gap: 15px;">
+      <div style="border: 1px solid #e2e8f0; padding: 14px; border-radius: 8px; background: #fafafa;">
+        <span style="font-weight: 700; color: #6b21a8; font-size: 12px; display: block; margin-bottom: 6px; text-transform: uppercase;">🧩 Structured Reasoning</span>
+        <span style="font-size: 13px; color: #4b5563; line-height: 1.5;">Learns rigorous problem decomposition and multi-step solution patterns from a strong teacher.</span>
+      </div>
+      <div style="border: 1px solid #e2e8f0; padding: 14px; border-radius: 8px; background: #fafafa;">
+        <span style="font-weight: 700; color: #6b21a8; font-size: 12px; display: block; margin-bottom: 6px; text-transform: uppercase;">📐 Math &amp; STEM Focus</span>
+        <span style="font-size: 13px; color: #4b5563; line-height: 1.5;">Trained on approximately 250,000 samples centered on mathematics and scientific reasoning.</span>
+      </div>
+      <div style="border: 1px solid #e2e8f0; padding: 14px; border-radius: 8px; background: #fafafa;">
+        <span style="font-weight: 700; color: #6b21a8; font-size: 12px; display: block; margin-bottom: 6px; text-transform: uppercase;">🧪 Cross-Domain Transfer</span>
+        <span style="font-size: 13px; color: #4b5563; line-height: 1.5;">Shows small post-training gains in programming and tool use despite receiving no coding-specific SFT data.</span>
+      </div>
+      <div style="border: 1px solid #e2e8f0; padding: 14px; border-radius: 8px; background: #fafafa;">
+        <span style="font-weight: 700; color: #6b21a8; font-size: 12px; display: block; margin-bottom: 6px; text-transform: uppercase;">⚙️ Practical Deployment</span>
+        <span style="font-size: 13px; color: #4b5563; line-height: 1.5;">Evaluated through vLLM NVFP4 and llama.cpp MTP-Q8_0 deployment paths.</span>
+      </div>
+    </div>
+  </div>
+</div>
+## 🤝 Collaboration & Training Support
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr)); gap: 14px; margin-bottom: 28px;">
+  <div style="border: 1px solid #a7f3d0; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
+    <div style="background: linear-gradient(135deg, #10b981 0%, #047857 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px;"><span>🧪</span> Hardware Cooperation &amp; Joint Collaboration</div>
+    <div style="padding: 16px; font-size: 13px; color: #334155; line-height: 1.7;">
+      This project is built in close collaboration with hardware engineer <b>Kyle Hessling</b>, whose infrastructure, training support, and evaluation assistance helped make this release possible.
+      <div style="margin-top: 10px;">👉 Follow his hardware and model-training updates on X / Twitter: <a href="https://x.com/KyleHessling1" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">@KyleHessling1</a></div>
+    </div>
+  </div>
+  <div style="border: 1px solid #ddd6fe; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
+    <div style="background: linear-gradient(135deg, #7c3aed 0%, #6d28d9 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px;"><span>🦥</span> Fine-tuning Framework (Unsloth)</div>
+    <div style="padding: 16px; font-size: 13px; color: #334155; line-height: 1.7;">
+      The model training workflow is accelerated and memory-optimized with <b>Unsloth</b>. Special thanks to the Unsloth team for making efficient large-model fine-tuning more accessible.
+      <div style="margin-top: 10px;">👉 Documentation and fine-tuning guidance: <a href="https://unsloth.ai/docs" target="_blank" style="color: #7c3aed; text-decoration: none; font-weight: 700;">unsloth.ai/docs</a></div>
+    </div>
+  </div>
+</div>
+## 🔬 1. Distillation & Training Data
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02); margin-bottom: 28px;">
+  <div style="background: linear-gradient(135deg, #0284c7 0%, #0369a1 100%); padding: 13px 17px; color: white; font-weight: 700; font-size: 14px;">🧬 From DeepSeek-V4-Pro to a Compact 9B Student</div>
+  <div style="padding: 18px; font-size: 13px; color: #334155; line-height: 1.75;">
+    <p style="margin: 0 0 12px 0;">The teacher data for this release was generated with <b>DeepSeek-V4-Pro (Max Effect)</b>. Approximately <b>250,000 mathematics and STEM samples</b> were used for supervised fine-tuning, with an emphasis on structured derivation, domain-aware problem decomposition, and reliable final-answer construction.</p>
+    <div style="background: #f0f9ff; border-left: 4px solid #0284c7; border-radius: 0 8px 8px 0; padding: 12px 14px; color: #0c4a6e;">
+      <b>Special training note:</b> no coding data was included in this fine-tuning run. Nevertheless, later testing showed a small generalization improvement in programming and tool-calling tasks. This should be interpreted as cross-domain transfer from stronger reasoning structure—not as evidence that the model received direct coding supervision.
+    </div>
+  </div>
+</div>
+## 📊 2. Benchmark Results
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 16px; overflow: hidden; background: #ffffff; box-shadow: 0 10px 15px -3px rgba(0,0,0,0.05); margin-bottom: 28px;">
+  <div style="background: linear-gradient(135deg, #0f766e 0%, #0369a1 100%); padding: 20px; color: white;">
+    <h3 style="margin: 0; font-size: 20px; font-weight: 700; color: white; border: none;">⚡ Performance Snapshot</h3>
+    <p style="margin: 5px 0 0 0; font-size: 13px; color: #cffafe;">Strong grade-school mathematics performance and the best average MMLU-Pro result among the tested 9B baselines.</p>
+  </div>
+  <div style="padding: 22px;">
+    <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(170px, 1fr)); gap: 15px;">
+      <div style="border: 1px solid #bae6fd; padding: 16px; border-radius: 10px; background: #f0f9ff; text-align: center;">
+        <span style="font-size: 11px; font-weight: 700; color: #0369a1; text-transform: uppercase; display: block; margin-bottom: 6px;">GSM8K</span>
+        <span style="font-size: 25px; font-weight: 800; color: #0c4a6e; display: block;">94.50%</span>
+        <span style="font-size: 11px; color: #64748b;">4-run average · NVFP4 · vLLM</span>
+      </div>
+      <div style="border: 1px solid #ddd6fe; padding: 16px; border-radius: 10px; background: #f5f3ff; text-align: center;">
+        <span style="font-size: 11px; font-weight: 700; color: #6d28d9; text-transform: uppercase; display: block; margin-bottom: 6px;">MMLU-Pro Math</span>
+        <span style="font-size: 25px; font-weight: 800; color: #4c1d95; display: block;">92.40%</span>
+        <span style="font-size: 11px; color: #64748b;">MTP-Q8_0 GGUF · 8K</span>
+      </div>
+      <div style="border: 1px solid #a7f3d0; padding: 16px; border-radius: 10px; background: #f0fdf4; text-align: center;">
+        <span style="font-size: 11px; font-weight: 700; color: #047857; text-transform: uppercase; display: block; margin-bottom: 6px;">MMLU-Pro Average</span>
+        <span style="font-size: 25px; font-weight: 800; color: #065f46; display: block;">90.53%</span>
+        <span style="font-size: 11px; color: #64748b;">Math · Physics · Chemistry</span>
+      </div>
+      <div style="border: 1px solid #fde68a; padding: 16px; border-radius: 10px; background: #fffbeb; text-align: center;">
+        <span style="font-size: 11px; font-weight: 700; color: #b45309; text-transform: uppercase; display: block; margin-bottom: 6px;">Gain vs Base</span>
+        <span style="font-size: 25px; font-weight: 800; color: #92400e; display: block;">+0.93</span>
+        <span style="font-size: 11px; color: #64748b;">MMLU-Pro average points</span>
+      </div>
+    </div>
+  </div>
+</div>
+### 2.1 GSM8K
+The NVFP4 checkpoint was evaluated on GSM8K **four times**, and the resulting scores were averaged. Its **four-run mean accuracy is 94.50%** under vLLM. In the comparison supplied with this release, the 9B student slightly exceeds the reported DeepSeek-V4-Pro teacher result and approaches the Llama-3.3-70B-Instruct reference.
+<div style="overflow-x: auto; margin-bottom: 24px;">
+<table style="display: table; width: 100%; table-layout: fixed; border-collapse: collapse; overflow-wrap: anywhere; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-size: 12px; line-height: 1.25; border: 1px solid #cbd5e1;">
+  <thead>
+    <tr style="background: #eff6ff;">
+      <th style="width: 72%; padding: 6px 10px !important; border-bottom: 2px solid #0284c7; text-align: left; color: #0369a1;">Model</th>
+      <th style="width: 28%; padding: 6px 10px !important; border-bottom: 2px solid #0284c7; text-align: right; color: #0369a1;">Accuracy</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0;"><b>MiMo-V2.5-Pro</b></td><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0; text-align: right;"><b>99.60%</b></td></tr>
+    <tr><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0;">Llama-3.1-405B-Instruct</td><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0; text-align: right;">96.80%</td></tr>
+    <tr><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0;">Llama-3.3-70B-Instruct</td><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0; text-align: right;">94.84%</td></tr>
+    <tr style="background: #f5f3ff;"><td style="padding: 6px 10px !important; border-bottom: 1px solid #ddd6fe; color: #5b21b6;"><b>DeepSeek-V4-Pro-Qwen3.5-9B</b></td><td style="padding: 6px 10px !important; border-bottom: 1px solid #ddd6fe; text-align: right; color: #5b21b6;"><b>94.50%</b></td></tr>
+    <tr><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0;">DeepSeek-V4-Pro</td><td style="padding: 6px 10px !important; border-bottom: 1px solid #e2e8f0; text-align: right;">92.60%</td></tr>
+    <tr><td style="padding: 6px 10px !important;">DeepSeek-V3</td><td style="padding: 6px 10px !important; text-align: right;">89.30%</td></tr>
+  </tbody>
+</table>
+</div>
+<div style="background: #f8fafc; border-left: 4px solid #64748b; padding: 11px 14px; border-radius: 0 8px 8px 0; color: #475569; font-size: 12px; line-height: 1.6; margin-bottom: 26px;"><b>Evaluation configuration:</b> DeepSeek-V4-Pro-Qwen3.5-9B-NVFP4 · NVFP4 (FP8 E4M3) · vLLM · four GSM8K test runs, with the final 94.50% reported as their arithmetic mean. The reference scores for the other models shown above are sourced from the <a href="https://huggingface.co/datasets/openai/gsm8k" style="color: #0369a1; font-weight: 700; text-decoration: none;">openai/gsm8k repository</a>.</div>
+### 2.2 MMLU-Pro: Math, Physics & Chemistry
+Each model was evaluated on **500 questions from each of three MMLU-Pro subsets**—Math, Physics, and Chemistry—for **1,500 questions per model**. In this reported comparison, **DeepSeek-V4-Pro-Qwen3.5-9B** achieves the highest average at **90.53%**. Its compact 4B sibling is now included alongside the Qwen and Claude Mythos-distilled references.
+<div style="overflow-x: auto; margin-bottom: 20px;">
+<table style="display: table; width: 100%; min-width: 760px; table-layout: auto; border-collapse: collapse; overflow-wrap: anywhere; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-size: 12px; line-height: 1.25; border: 1px solid #cbd5e1;">
+  <thead>
+    <tr style="background: #f5f3ff;">
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: left; color: #6d28d9;">Model</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: left; color: #6d28d9;">Evaluation Build</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: right; color: #6d28d9;">Math</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: right; color: #6d28d9;">Physics</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: right; color: #6d28d9;">Chemistry</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #7c3aed; text-align: right; color: #6d28d9;">Average</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="background: #f5f3ff;">
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe; color: #5b21b6;"><b>DeepSeek-V4-Pro-Qwen3.5-9B</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe;">MTP-Q8_0 GGUF</td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe; text-align: right;"><b>92.40%</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe; text-align: right;"><b>89.40%</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe; text-align: right;"><b>89.80%</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #ddd6fe; text-align: right; color: #5b21b6;"><b>90.53%</b></td>
+    </tr>
+    <tr>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Qwen3.5-9B</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">MTP-Q8_0 GGUF</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">90.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">89.00%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">89.20%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">89.60%</td>
+    </tr>
+    <tr>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Claude Mythos-distilled 27B</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">MTP-Q8 GGUF</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">88.40%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">87.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">82.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">86.20%</td>
+    </tr>
+    <tr>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Claude Mythos-distilled 9B</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">MTP-Q8_0 GGUF</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">86.00%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">78.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">78.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">81.07%</td>
+    </tr>
+    <tr style="background: #f0fdf4;">
+      <td style="padding: 10px 12px; color: #065f46;"><b>DeepSeek-V4-Pro-Qwen3.5-4B</b></td>
+      <td style="padding: 10px 12px;">MTP-Q8 GGUF</td>
+      <td style="padding: 10px 12px; text-align: right;"><b>80.40%</b></td>
+      <td style="padding: 10px 12px; text-align: right;"><b>74.20%</b></td>
+      <td style="padding: 10px 12px; text-align: right;"><b>74.80%</b></td>
+      <td style="padding: 10px 12px; text-align: right; color: #065f46;"><b>76.47%</b></td>
+    </tr>
+  </tbody>
+</table>
+</div>
+<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); gap: 14px; margin-bottom: 28px; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;">
+  <div style="border: 1px solid #a7f3d0; border-radius: 10px; background: #f0fdf4; padding: 14px; color: #065f46; font-size: 13px; line-height: 1.6;"><b>📈 vs. Qwen3.5-9B</b><br>+1.80 points in Math, +0.40 in Physics, +0.60 in Chemistry, and <b>+0.93 average points</b>.</div>
+  <div style="border: 1px solid #bae6fd; border-radius: 10px; background: #f0f9ff; padding: 14px; color: #0c4a6e; font-size: 13px; line-height: 1.6;"><b>🧪 Evaluation setup</b><br>500 questions per subset · 1,500 total per model · llama.cpp. The 9B rows use MTP-Q8_0 GGUF at 8K context, the 4B student uses MTP-Q8 GGUF at 64K context, and Claude Mythos-distilled 27B uses MTP-Q8 GGUF at 32K context.</div>
+</div>
+### 2.3 Inference Efficiency
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 16px; overflow: hidden; background: #ffffff; box-shadow: 0 10px 15px -3px rgba(0,0,0,0.05); margin-bottom: 28px;">
+  <div style="background: linear-gradient(135deg, #0f766e 0%, #047857 55%, #0369a1 100%); padding: 20px; color: white;">
+    <h3 style="margin: 0; font-size: 20px; font-weight: 800; color: white; border: none;">⚡ More Correct Answers per Reasoning Token</h3>
+    <p style="margin: 6px 0 0 0; font-size: 13px; color: #ccfbf1; line-height: 1.6;">On the same 1,500-question MMLU-Pro sample, the DeepSeek-distilled model combines the highest accuracy with the lowest token cost per correct answer.</p>
+  </div>
+  <div style="padding: 20px;">
+    <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr)); gap: 14px;">
+      <div style="border: 1px solid #a7f3d0; border-radius: 10px; background: #f0fdf4; padding: 15px; color: #065f46; font-size: 13px; line-height: 1.6;">
+        <span style="font-size: 11px; font-weight: 800; color: #047857; text-transform: uppercase; letter-spacing: 0.4px;">vs. Qwen3.5-9B</span><br>
+        <span style="font-size: 22px; font-weight: 800;">36.1% fewer tokens</span><br>
+        per correct answer, with <b>+0.93 accuracy points</b>.
+      </div>
+      <div style="border: 1px solid #bae6fd; border-radius: 10px; background: #f0f9ff; padding: 15px; color: #0c4a6e; font-size: 13px; line-height: 1.6;">
+        <span style="font-size: 11px; font-weight: 800; color: #0369a1; text-transform: uppercase; letter-spacing: 0.4px;">vs. Claude Mythos-distilled 9B</span><br>
+        <span style="font-size: 22px; font-weight: 800;">22.7% fewer tokens</span><br>
+        per correct answer, with <b>+9.47 accuracy points</b>.
+      </div>
+      <div style="border: 1px solid #ddd6fe; border-radius: 10px; background: #f5f3ff; padding: 15px; color: #4c1d95; font-size: 13px; line-height: 1.6;">
+        <span style="font-size: 11px; font-weight: 800; color: #6d28d9; text-transform: uppercase; letter-spacing: 0.4px;">Correct answers / 1M tokens</span><br>
+        <span style="font-size: 22px; font-weight: 800;">316.8</span><br>
+        <b>+56.4%</b> vs. Qwen and <b>+29.3%</b> vs. Claude Mythos-distilled 9B.
+      </div>
+    </div>
+  </div>
+</div>
+<div style="overflow-x: auto; margin-bottom: 20px;">
+<table style="display: table; width: 100%; min-width: 900px; table-layout: auto; border-collapse: collapse; overflow-wrap: anywhere; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-size: 12px; line-height: 1.25; border: 1px solid #cbd5e1;">
+  <thead>
+    <tr style="background: #ecfdf5;">
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: left; color: #047857;">Model</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: right; color: #047857;">Accuracy</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: right; color: #047857;">Avg. Reasoning<br>Tokens / Question</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: right; color: #047857;">Median</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: right; color: #047857;">Tokens /<br>Correct Answer</th>
+      <th style="padding: 10px 12px; border-bottom: 2px solid #10b981; text-align: right; color: #047857;">Correct Answers /<br>1M Tokens</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="background: #f0fdf4;">
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; color: #065f46;"><b>DeepSeek-V4-Pro-Qwen3.5-9B</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; text-align: right; color: #065f46;"><b>90.53%</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; text-align: right; color: #065f46;"><b>2,858</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; text-align: right;">676</td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; text-align: right; color: #065f46;"><b>3,157</b></td>
+      <td style="padding: 10px 12px; border-bottom: 1px solid #a7f3d0; text-align: right; color: #065f46;"><b>316.8</b></td>
+    </tr>
+    <tr>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Qwen3.5-9B</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">89.60%</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">4,425</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">3,214</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">4,938</td>
+      <td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right;">202.5</td>
+    </tr>
+    <tr>
+      <td style="padding: 9px 12px;">Claude Mythos-distilled 9B</td>
+      <td style="padding: 9px 12px; text-align: right;">81.07%</td>
+      <td style="padding: 9px 12px; text-align: right;">3,310</td>
+      <td style="padding: 9px 12px; text-align: right;"><b>639</b></td>
+      <td style="padding: 9px 12px; text-align: right;">4,083</td>
+      <td style="padding: 9px 12px; text-align: right;">244.9</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); gap: 15px; margin-bottom: 28px; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;">
+  <div style="border: 1px solid #ddd6fe; border-radius: 12px; overflow: hidden; background: #ffffff;">
+    <div style="background: #f5f3ff; border-bottom: 1px solid #ddd6fe; padding: 11px 14px; color: #6d28d9; font-size: 13px; font-weight: 800;">🧪 Token Savings by MMLU-Pro Subject</div>
+    <div style="overflow-x: auto;">
+      <table style="display: table; width: 100%; min-width: 430px; table-layout: auto; border-collapse: collapse; overflow-wrap: anywhere; font-size: 12px; line-height: 1.25; color: #334155;">
+        <thead><tr style="background: #fafafa;"><th style="padding: 9px 12px; text-align: left; border-bottom: 1px solid #e2e8f0;">Subject</th><th style="padding: 9px 12px; text-align: right; border-bottom: 1px solid #e2e8f0;">vs. Qwen3.5-9B</th><th style="padding: 9px 12px; text-align: right; border-bottom: 1px solid #e2e8f0;">vs. Claude Mythos-distilled 9B</th></tr></thead>
+        <tbody>
+          <tr><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Math</td><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right; color: #047857;"><b>47.4%</b></td><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right; color: #047857;"><b>35.6%</b></td></tr>
+          <tr><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0;">Physics</td><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right; color: #047857;"><b>30.8%</b></td><td style="padding: 9px 12px; border-bottom: 1px solid #e2e8f0; text-align: right; color: #047857;"><b>10.8%</b></td></tr>
+          <tr><td style="padding: 9px 12px;">Chemistry</td><td style="padding: 9px 12px; text-align: right; color: #047857;"><b>32.1%</b></td><td style="padding: 9px 12px; text-align: right; color: #047857;"><b>23.3%</b></td></tr>
+        </tbody>
+      </table>
+    </div>
+  </div>
+  <div style="border: 1px solid #bae6fd; border-radius: 12px; overflow: hidden; background: #ffffff;">
+    <div style="background: #f0f9ff; border-bottom: 1px solid #bae6fd; padding: 11px 14px; color: #0369a1; font-size: 13px; font-weight: 800;">📐 GSM8K NVFP4 Reasoning Profile</div>
+    <div style="padding: 14px;">
+      <div style="display: grid; grid-template-columns: repeat(2, minmax(110px, 1fr)); gap: 10px;">
+        <div style="background: #f8fafc; border-radius: 8px; padding: 10px;"><span style="font-size: 10px; color: #64748b; text-transform: uppercase; font-weight: 700;">Average</span><br><b style="font-size: 18px; color: #0c4a6e;">1,627</b><span style="font-size: 11px; color: #64748b;"> tokens</span></div>
+        <div style="background: #f8fafc; border-radius: 8px; padding: 10px;"><span style="font-size: 10px; color: #64748b; text-transform: uppercase; font-weight: 700;">Median</span><br><b style="font-size: 18px; color: #0c4a6e;">372</b><span style="font-size: 11px; color: #64748b;"> tokens</span></div>
+        <div style="background: #f8fafc; border-radius: 8px; padding: 10px;"><span style="font-size: 10px; color: #64748b; text-transform: uppercase; font-weight: 700;">P90</span><br><b style="font-size: 18px; color: #0c4a6e;">3,042</b><span style="font-size: 11px; color: #64748b;"> tokens</span></div>
+        <div style="background: #f8fafc; border-radius: 8px; padding: 10px;"><span style="font-size: 10px; color: #64748b; text-transform: uppercase; font-weight: 700;">Per Correct Answer</span><br><b style="font-size: 18px; color: #0c4a6e;">1,721</b><span style="font-size: 11px; color: #64748b;"> tokens</span></div>
+      </div>
+      <p style="margin: 11px 0 0 0; font-size: 11px; color: #64748b; line-height: 1.5;">Computed from <b>2,638</b> available raw NVFP4 trajectories.</p>
+    </div>
+  </div>
+</div>
+<div style="background: #f8fafc; border-left: 4px solid #64748b; padding: 11px 14px; border-radius: 0 8px 8px 0; color: #475569; font-size: 12px; line-height: 1.6; margin-bottom: 28px;"><b>Metric notes:</b> MMLU-Pro efficiency uses 1,500 questions per model and the supplied composite score for the final correct-answer total. “Tokens per correct answer” is total reasoning tokens divided by correct answers; “correct answers per 1M tokens” is the inverse throughput measure. Lower is better for token-cost metrics; higher is better for accuracy and correct-answer throughput. Comparison deltas follow the supplied composite-result calculations, so recomputing them only from the rounded display values may produce a 0.01-point difference. GSM8K statistics summarize the currently available NVFP4 raw trajectories and are reported separately from the MMLU-Pro MTP-Q8_0 comparison.</div>
+## 🛠️ 3. Generalization Beyond the Training Mix
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; margin-bottom: 28px;">
+  <div style="background: linear-gradient(135deg, #10b981 0%, #047857 100%); padding: 13px 17px; color: white; font-weight: 700; font-size: 14px;">🔁 A Small but Interesting Transfer Effect</div>
+  <div style="padding: 18px; color: #334155; font-size: 13px; line-height: 1.7;">
+    Although the SFT mixture intentionally contained <b>no coding data</b>, post-training tests indicated a <b>small improvement in programming and tool-calling behavior</b>. A plausible interpretation is that mathematics and STEM supervision strengthened reusable skills such as decomposition, constraint tracking, verification, and structured action planning.
+    <br><br>
+    This observation is preliminary. It should not be read as a claim that the model is coding-specialized, and the release does not replace dedicated code benchmarks or agent-environment evaluation.
+  </div>
+</div>
+## 🎯 4. Recommended Uses
+- Mathematical problem solving and step-by-step derivation
+- Physics, chemistry, and broader STEM question answering
+- Structured reasoning and analytical instruction following
+- Research on reasoning distillation and cross-domain SFT transfer
+- Experimental tool-calling or programming workflows where outputs are independently validated
+## ⚠️ 5. Limitations
+- This is an experimental 9B community model and remains subject to hallucinations, reasoning errors, and unstable behavior on difficult or underspecified tasks.
+- The reported MMLU-Pro results are based on 500 randomly sampled questions from each of the Math, Physics, and Chemistry subsets, rather than the complete subsets; they are not a comprehensive capability or safety evaluation.
+- GSM8K and MMLU-Pro used different inference builds and backends, so their scores should not be used to infer quantization equivalence.
+- Programming and tool-use gains were observed without direct coding supervision but are described only as small, preliminary generalization gains.
+- Users should independently verify high-stakes mathematical, scientific, or factual outputs.
+## 📚 6. Resources, Acknowledgements & Citation
+<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 14px; overflow: hidden; background: #ffffff; margin-top: 18px;">
+  <div style="background: linear-gradient(135deg, #7c3aed 0%, #4c1d95 100%); padding: 16px 20px; color: white; font-weight: 800; font-size: 16px;">🌟 Open Research Release</div>
+  <div style="padding: 20px; color: #334155; font-size: 13px; line-height: 1.7;">
+    <p style="margin-top: 0;"><b>Fine-tuning guide:</b> <a href="https://github.com/R6410418/Jackrong-llm-finetuning-guide" style="color: #7c3aed; font-weight: 700; text-decoration: none;">Jackrong LLM Fine-Tuning Guide</a></p>
+    <p><b>Acknowledgements:</b> Kyle Hessling for hardware collaboration, training support, and evaluation assistance; DeepSeek for the teacher model and reasoning data source; Qwen for the base model family; Unsloth for efficient fine-tuning tooling; and the open-source inference community behind vLLM and llama.cpp.</p>
+    <p><b>Citation:</b></p>
+    <pre style="background: #f5f3ff; color: #4c1d95; border: 1px solid #ddd6fe; padding: 14px; border-radius: 8px; overflow-x: auto; font-size: 12px;"><code>@misc{jackrong_deepseek_v4_pro_qwen35_9b,
+  title     = {DeepSeek-V4-Pro-Qwen3.5-9B},
+  author    = {Jackrong},
+  year      = {2026},
+  publisher = {Hugging Face}
+}</code></pre>
+  </div>
+</div>