Instructions to use reaperdoesntknow/Qwen3-1.7B-Thinking-Distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/Qwen3-1.7B-Thinking-Distil with Transformers:

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

pipe = pipeline("text-generation", model="reaperdoesntknow/Qwen3-1.7B-Thinking-Distil")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/Qwen3-1.7B-Thinking-Distil")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Qwen3-1.7B-Thinking-Distil", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reaperdoesntknow/Qwen3-1.7B-Thinking-Distil with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil

SGLang

How to use reaperdoesntknow/Qwen3-1.7B-Thinking-Distil 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 "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use reaperdoesntknow/Qwen3-1.7B-Thinking-Distil with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil
```

reaperdoesntknow commited on Mar 30

Commit

679b37d

verified ·

1 Parent(s): 5753cfe

Complete model card — training details, usage, collection cross-links

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@@ -1,252 +1,163 @@
 ---
 library_name: transformers
 tags:
-- trl
 - sft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-### Testing Data, Factors & Metrics
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-<!-- This should link to a Dataset Card if possible. -->
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
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-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
----
-## Convergent Intelligence Portfolio
-*Part of the [Qwen3 1.7B Distillation Series](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
-### Related Models
-| Model | Downloads | Format |
-|-------|-----------|--------|
-| [Qwen3-1.7B-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B) | 96 | HF |
-| [Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) | 65 | HF |
-| [Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF) | 175 | GGUF |
-### Top Models from Our Lab
-| Model | Downloads |
-|-------|-----------|
-| [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 |
-| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 |
-| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 |
-| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
-| [SMOLM2Prover-GGUF](https://huggingface.co/reaperdoesntknow/SMOLM2Prover-GGUF) | 150 |
-**Total Portfolio: 41 models | 2,781 total downloads**
-*Last updated: 2026-03-28 12:46 UTC*
-<!-- CIX-CROSSLINK-START -->
 ---
-## From the Convergent Intelligence Portfolio
-**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.
-Top model: [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) — 508 downloads
-Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)
-*Convergent Intelligence LLC: Research Division*
-<!-- CIX-CROSSLINK-END -->
----
-<sub>Part of the [reaperdoesntknow research portfolio](https://huggingface.co/reaperdoesntknow) — 48 models, 12,094 total downloads | Last refreshed: 2026-03-29 21:04 UTC</sub>
-<!-- cix-keeper-ts:2026-03-30T02:43:02Z -->
-<!-- card-refresh: 2026-03-30 -->

 ---
+license: apache-2.0
 library_name: transformers
+pipeline_tag: text-generation
 tags:
+- qwen3
 - sft
+- trl
+- knowledge-distillation
+- thinking
+- longwriter
+- convergent-intelligence
+base_model: Qwen/Qwen3-1.7B
+datasets:
+- longwriter-6k
 ---
+# Qwen3-1.7B-Thinking-Distil
+**Extended Reasoning Distillation from Qwen3-30B-A3B-Thinking → 1.7B**
+*Convergent Intelligence LLC: Research Division*
+---
+## What This Is
+The most downloaded model in the Convergent Intelligence portfolio. Qwen3-1.7B-Thinking-Distil captures extended deliberation patterns from the Qwen3-30B-A3B **Thinking** teacher — the variant that generates long-form reasoning chains before committing to an answer — and compresses them into a 1.7B student via supervised fine-tuning on the [longwriter-6k](https://huggingface.co/datasets/longwriter-6k) dataset.
+The Thinking teacher produces the **richest signal** of the three teacher variants in the DistilQwen family (Instruct, Thinking, Coder). Where Instruct distillation captures clean instruction-following and Coder captures hierarchical decomposition, Thinking distillation captures the extended internal monologue — the model reasoning through uncertainty, backtracking, and re-evaluating before arriving at a conclusion. That deliberative depth is what makes this variant the highest-download model in the collection.
+## Architecture
+| Parameter | Value |
+|-----------|-------|
+| Architecture | Qwen3ForCausalLM |
+| Parameters | ~2.03B (1.7B effective) |
+| Hidden Size | 2048 |
+| Layers | 28 |
+| Attention Heads | 16 (Q) / 8 (KV) — GQA |
+| Intermediate | 6144 |
+| Head Dimension | 128 |
+| Context Length | 40,960 tokens (max position) |
+| Vocabulary | 151,936 |
+| Precision | BF16 |
+| Activation | SiLU |
+## Training
+**Teacher:** Qwen3-30B-A3B-Thinking
+**Student:** Qwen3-1.7B
+**Dataset:** longwriter-6k — long-form generation samples that preserve extended reasoning chains
+**Method:** Supervised Fine-Tuning (SFT) via TRL
+| Parameter | Value |
+|-----------|-------|
+| Max Sequence Length | 4,096 |
+| Precision | BF16 |
+| Framework | TRL (SFTTrainer) |
+| Hardware | NVIDIA H100 |
+The training captures the teacher's extended thinking traces through direct SFT rather than logit-level KD. This is a deliberate design choice — the longwriter-6k dataset provides naturally long reasoning samples where the signal is in the structure of the generation (how the teacher approaches, reconsiders, and resolves), not just the final token probabilities.
+For the full topology-aware distillation pipeline (BV decomposition, jump detection, curriculum ordering), see [TopologicalQwen](https://huggingface.co/reaperdoesntknow/TopologicalQwen). This model is the SFT-direct variant — simpler, faster to train, and empirically the most downloaded for a reason: the Thinking teacher's extended chains transfer well through pure SFT.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "reaperdoesntknow/Qwen3-1.7B-Thinking-Distil"
+)
+messages = [
+    {"role": "user", "content": "Explain why gradient descent can get stuck in saddle points but not local minima in high dimensions."}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+output = model.generate(
+    **inputs,
+    max_new_tokens=2048,
+    do_sample=True,
+    top_p=0.9,
+    temperature=0.7,
+    repetition_penalty=1.15
+)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+### Generation Tips
+- **Temperature 0.6–0.8** works best for reasoning tasks — low enough for coherence, high enough to activate the extended deliberation patterns from the Thinking teacher.
+- **Repetition penalty 1.1–1.2** prevents the model from getting caught in reasoning loops during long generations.
+- **Max tokens 1024–2048** — the model was trained on 4096 max seq, so it can generate long. Give it room.
+- The model inherits the Thinking teacher's tendency to reason before answering. Let it.
+## Distillation Position
+```
+Qwen3-30B-A3B-Thinking (teacher)
+  ↓ SFT on longwriter-6k (4096 max seq)
+Qwen3-1.7B-Thinking-Distil ← you are here
+```
+This model is the **direct SFT** path. The DistilQwen collection also includes models that go through additional refinement stages:
+```
+Qwen3-1.7B (base)
+  → Qwen3-1.7B-Distilled-30B-A3B (Instruct teacher KD)
+    → DiStil (uncensored SFT)
+      → Disctil (DISC refinement)
+        → TopologicalQwen (full TKD pipeline)
+```
+Different paths, different capabilities. This model prioritizes extended reasoning. TopologicalQwen prioritizes structural precision. The Coder variant prioritizes hierarchical decomposition. They're complementary.
+## DistilQwen Collection
+| Model | Downloads | What It Does |
+|-------|-----------|-------------|
+| **[Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil)** | **1,188** | **← this model. Thinking teacher SFT.** |
+| [TopologicalQwen](https://huggingface.co/reaperdoesntknow/TopologicalQwen) | 1,134 | Full TKD pipeline. BV decomposition + DualMind format. |
+| [DiStil-Qwen3-1.7B-uncensored](https://huggingface.co/reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored) | 1,030 | DISC-informed uncensored distillation. |
+| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 966 | Coder teacher. Hierarchical problem solving. |
+| [DistilQwen3-1.7B-uncensored](https://huggingface.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored) | 832 | Base uncensored variant. |
+Full collection: [DistilQwen on HuggingFace](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)
+## Methodology
+Full methodology paper: **[Structure Over Scale: Proof-Weighted Knowledge Distillation](https://doi.org/10.57967/hf/8165)** (DOI: 10.57967/hf/8165)
+Companion paper: **[Three Teachers to Dual Cognition](https://doi.org/10.57967/hf/8184)** (DOI: 10.57967/hf/8184) — covers the DualMind extension and ghost imprinting phenomenon.
+## License
+Apache 2.0 — same as the base Qwen3 model.
+## Citation
+```bibtex
+@misc{colca2026distilqwen,
+  title={Structure Over Scale: Proof-Weighted Knowledge Distillation from Qwen3-30B to 1.7B},
+  author={Colca, Roy},
+  year={2026},
+  doi={10.57967/hf/8165},
+  publisher={Convergent Intelligence LLC: Research Division}
+}
+```
 ---
+*Convergent Intelligence LLC: Research Division — 49 models, 22,598 downloads across the portfolio.*
+*[Full portfolio](https://huggingface.co/reaperdoesntknow) | [DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c) | [DualMind Collection](https://huggingface.co/collections/reaperdoesntknow/dualmind-69c93f888c6e79ecc69cf41e)*