etcode_opus_distilled_v1

Instructions to use buraksusam123/etcode_opus_distilled_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use buraksusam123/etcode_opus_distilled_v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="buraksusam123/etcode_opus_distilled_v1",
	filename="Qwen3.5-27B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use buraksusam123/etcode_opus_distilled_v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf buraksusam123/etcode_opus_distilled_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 buraksusam123/etcode_opus_distilled_v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf buraksusam123/etcode_opus_distilled_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 buraksusam123/etcode_opus_distilled_v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Use Docker

docker model run hf.co/buraksusam123/etcode_opus_distilled_v1:Q4_K_M

LM Studio
Jan

vLLM

How to use buraksusam123/etcode_opus_distilled_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "buraksusam123/etcode_opus_distilled_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": "buraksusam123/etcode_opus_distilled_v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Ollama
How to use buraksusam123/etcode_opus_distilled_v1 with Ollama:
```
ollama run hf.co/buraksusam123/etcode_opus_distilled_v1:Q4_K_M
```

Unsloth Studio

How to use buraksusam123/etcode_opus_distilled_v1 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 buraksusam123/etcode_opus_distilled_v1 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 buraksusam123/etcode_opus_distilled_v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for buraksusam123/etcode_opus_distilled_v1 to start chatting

How to use buraksusam123/etcode_opus_distilled_v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "buraksusam123/etcode_opus_distilled_v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use buraksusam123/etcode_opus_distilled_v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf buraksusam123/etcode_opus_distilled_v1:Q4_K_M

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 buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use buraksusam123/etcode_opus_distilled_v1 with Docker Model Runner:
```
docker model run hf.co/buraksusam123/etcode_opus_distilled_v1:Q4_K_M
```

Lemonade

How to use buraksusam123/etcode_opus_distilled_v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull buraksusam123/etcode_opus_distilled_v1:Q4_K_M

Run and chat with the model

lemonade run user.etcode_opus_distilled_v1-Q4_K_M

List all available models

lemonade list

etcode_opus_distilled_v1 / README.md

buraksusam123

Duplicate from Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF

754ff3e about 1 month ago

preview code

raw

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	---
	language:
	- en
	- zh
	license: apache-2.0
	base_model: Qwen/Qwen3.5-27B
	tags:
	- unsloth
	- qwen
	- qwen3.5
	- reasoning
	- chain-of-thought
	- Dense
	pipeline_tag: image-text-to-text
	datasets:
	- nohurry/Opus-4.6-Reasoning-3000x-filtered
	- Jackrong/Qwen3.5-reasoning-700x
	---
	# 🌟 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
	🔥 Update (April 5): I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process.

	> ❤️ Special thanks to the [Unsloth](https://unsloth.ai) open-source library and [@KyleHessling1](https://x.com/kylehessling1) for their support.

	## 📚 Resources & Guides

	👉 [GitHub Repository: Jackrong-llm-finetuning-guide](https://github.com/R6410418/Jackrong-llm-finetuning-guide.git)
	Visit the repo to dive into the codebase and reproduce the results locally or on Colab.

	### 📥 Core Technical Document
	🔗 [Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)](https://github.com/R6410418/Jackrong-llm-finetuning-guide/blob/main/guidePDF/Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf)
	* The Full Pipeline: A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face.
	* Beginner Friendly: Includes an introductory guide to getting started with Google Colab and Unsloth.
	* Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.

	> A Note:
	> My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity.
	>
	> No one starts as an expert, but every expert was once brave enough to begin.
	>
	> All training and testing for this project were self-funded. If you find this model or guide helpful, a Star ⭐️ on GitHub would be the greatest encouragement. Thank you! 🙏

	> [!Note]
	> The Claude series model optimizations are named under the Qwopus3.5 series, with the latest version being 🌟Qwopus3.5-v3.

	---
	# 🌟 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

	> Build Environment Upgrades:
	> - Fine-tuning Framework: Unsloth 2026.3.3
	> - Core Dependencies: Transformers 5.2.0
	> - This model fixes the crash in the official model caused by the Jinja template not supporting the "developer" role. (commonly sent by modern coding agents like Claude Code and OpenCode)
	> - It does not disable thinking mode by default, and allowing the agent to run continuously for over 9 minutes without interruption.
	> - Compared to the original model, autonomy and stability are significantly improved.

	![HB8AleUaMAArNyM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/GHkMJL6I383eIwK1qj80K.jpeg)


	## 💡 Model Introduction
	Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a highly capable reasoning model fine-tuned on top of the powerful Qwen3.5 architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions.

	Through Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted `<think>` tags, and ultimately delivering precise, nuanced solutions.

	### 🧠 Example of Learned Reasoning Scaffold（Example）

	The model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern:
	“Let me analyze this request carefully: 1..2..3...”.
	This streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.

	```text
	Let me analyze this request carefully:

	1. Identify the core objective of the problem.
	2. Break the task into clearly defined subcomponents.
	3. Evaluate constraints and edge cases.
	4. Formulate a step-by-step solution plan.
	5. Execute the reasoning sequentially and verify consistency.
	.
	.
	.
	```

	---

	## 🗺️ Training Pipeline Overview

	```text
	Base Model (Qwen3.5-27B)
	│
	▼
	Supervised Fine-Tuning (SFT) + LoRA
	│
	▼
	Final Model (Claude-4.6-Opus-Reasoning-Distilled,text-only)
	```

	## 📋 Stage Details

	🔧Tool Calling Benchmark（benchmark tests by user @Chris Klaus）

	![Screenshot 2026-03-24 at 10.19.28 AM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/TjfbXq5AahoMj8xZuFDig.png)

	> From the test results, it is clear that different Qwen3.5 quantized models show significant differences in tool-calling capability. Among them, only the 27B model distilled with Claude Opus reasoning demonstrates stable performance.

	---

	🔥Community-tested advantages (benchmark tests by user @sudoing on a single RTX 3090):

	Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled shows significant advantages in coding-agent environments such as Claude Code and OpenCode:

	>- Native support for the “developer” role, requiring no Jinja template patches or ChatML workarounds.
	>- Thinking mode fully preserved (logs confirm `thinking=1`), not silently disabled, maintaining the complete chain-of-thought reasoning process.
	>- Greatly improved autonomy and stability — capable of running continuously for over 9 minutes autonomously (with zero human intervention). It actively waits for tool responses, reads outputs, self-corrects errors, and can even automatically generate a README, whereas the base model often stalls or freezes mid-execution.

	>Hardware usage remains unchanged:
	>- About 16.5 GB VRAM with Q4_K_M quantization
	>- 29–35 tok/s generation speed
	>- Full 262K context with no compromises

	- These improvements come from successfully distilling the structured reasoning style of Claude 4.6 Opus, allowing Qwopus to be truly plug-and-play in modern local coding agents and deliver an experience close to Opus in smoothness and usability.


	---

	### 🔹 Supervised Fine-Tuning (SFT)
	- Objective: To inject high-density reasoning logic and establish a strict format for problem-solving involving an internal thinking state prior to outputting the final response.
	- Methodology: We utilized Unsloth for highly efficient memory and compute optimization. A critical component of this stage is the `train_on_responses_only` strategy, masking instructions so the loss is purely calculated over the generation of the `<think>` sequences and the subsequent solutions.
	- Format Enforcement: All training samples were systematically normalized so the model strictly abides by the structure `<think> {internal reasoning} </think>\n {final answer}`.

	### 📚 All Datasets Used
	The dataset consists of high-quality, filtered reasoning distillation data:

	\| Dataset Name \| Description / Purpose \|
	\|--------------\|-----------------------\|
	\| [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) \| Provides comprehensive Claude 4.6 Opus reasoning trajectories. \|
	\| [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) \| Additional curated reasoning samples designed to strengthen structured step-by-step problem solving and improve reasoning diversity. \|

	## 🌟 Core Skills & Capabilities
	1. Modular & Structured Thinking: Inheriting traits from Opus-level reasoning, the model demonstrates confident parsing of the prompt, establishing an outlined plan in its `<think>` block sequentially rather than exploratory "trial-and-error" self-doubt.

	## ⚠️ Limitations & Intended Use
	- Hallucination Risk: While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.
	- Intended Scenario: Best suited for offline analytical tasks, coding, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic.
	- Preview Version Notice: Because this model is relatively new and intentionally lightweight, the surrounding ecosystem — including inference templates, fine-tuning pipelines, routing configurations, and tooling integrations — may not yet be fully mature or standardized. As a result, users may encounter occasional bugs, compatibility inconsistencies, or integration edge cases. The current release should be considered a preview build while the broader architectural stack and supporting utilities continue to stabilize and improve.

	## 🙏 Acknowledgements
	Significant thanks to the [Unsloth AI](https://unsloth.ai/) team for making rapid fine-tuning of MoE and large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets (`nohurry` and `TeichAI`).

	## 📖 Citation

	If you use this model in your research or projects, please cite:

	```bibtex
	@misc{jackrong_qwen35_opus_distilled,
	title = {Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled},
	author = {Jackrong},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled}}
	}
	```