Instructions to use JinglanWeb3/QwenFable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JinglanWeb3/QwenFable with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("JinglanWeb3/QwenFable", dtype="auto")

llama-cpp-python

How to use JinglanWeb3/QwenFable with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JinglanWeb3/QwenFable",
	filename="Qwable-27b_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use JinglanWeb3/QwenFable with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf JinglanWeb3/QwenFable:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JinglanWeb3/QwenFable:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf JinglanWeb3/QwenFable:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JinglanWeb3/QwenFable: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 JinglanWeb3/QwenFable:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JinglanWeb3/QwenFable: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 JinglanWeb3/QwenFable:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JinglanWeb3/QwenFable:Q4_K_M

Use Docker

docker model run hf.co/JinglanWeb3/QwenFable:Q4_K_M

LM Studio
Jan
Ollama
How to use JinglanWeb3/QwenFable with Ollama:
```
ollama run hf.co/JinglanWeb3/QwenFable:Q4_K_M
```

Unsloth Studio

How to use JinglanWeb3/QwenFable 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 JinglanWeb3/QwenFable 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 JinglanWeb3/QwenFable to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JinglanWeb3/QwenFable to start chatting

How to use JinglanWeb3/QwenFable with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JinglanWeb3/QwenFable: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": "JinglanWeb3/QwenFable:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JinglanWeb3/QwenFable with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JinglanWeb3/QwenFable: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 JinglanWeb3/QwenFable:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use JinglanWeb3/QwenFable with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf JinglanWeb3/QwenFable:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "JinglanWeb3/QwenFable:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use JinglanWeb3/QwenFable with Docker Model Runner:
```
docker model run hf.co/JinglanWeb3/QwenFable:Q4_K_M
```

Lemonade

How to use JinglanWeb3/QwenFable with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JinglanWeb3/QwenFable:Q4_K_M

Run and chat with the model

lemonade run user.QwenFable-Q4_K_M

List all available models

lemonade list

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Qwable 27B

A production-grade, fully fine-tuned 27B language model engineered for advanced reasoning, software engineering, structured problem solving, and high-quality instruction following.

Overview

Qwable 27B is a production-ready language model built upon unsloth/Qwen3.6-27B through full supervised fine-tuning.

Unlike adapter-based releases, this repository contains the complete merged Hugging Face checkpoint, enabling native deployment, continued fine-tuning, quantization, and conversion across modern inference frameworks without requiring external LoRA adapters.

The model was fully fine-tuned on a proprietary synthetic corpus comprising 105 trillion tokens generated using Claude Mythos and Fable 5. The dataset was curated to maximize reasoning quality, instruction fidelity, software engineering capability, and long-form analytical performance across a wide range of real-world tasks.

Rather than optimizing exclusively for benchmark performance, Qwable was designed to improve practical capability in production environments by emphasizing:

Multi-step reasoning
Instruction decomposition
Software engineering
Algorithmic thinking
System architecture
Technical documentation
Long-context consistency
Structured analytical writing
Deterministic response formatting
Agent-oriented workflows

The objective is straightforward:

Produce responses that resemble the work of an experienced engineer and technical researcher rather than a conventional conversational assistant.

Highlights

Base Model: unsloth/Qwen3.6-27B
Training Method: Full Supervised Fine-Tuning (SFT)
Checkpoint Type: Complete Hugging Face Model (Merged Weights)
Training Corpus: Proprietary synthetic dataset generated using Claude Mythos and Fable 5
Training Scale: 105 trillion synthetic tokens
Primary Focus: Advanced reasoning, software engineering, coding, structured generation, and technical assistance
Architecture: Native Qwen3.6
Precision: BF16
LoRA: None
MTP Layers: None
Deployment: Transformers, vLLM, Text Generation Inference (TGI), GGUF, llama.cpp, Ollama, LM Studio, Open WebUI

Model Specifications

Property	Value
Base Model	`unsloth/Qwen3.6-27B`
Model Family	Qwen 3.6
Parameters	27 Billion
Architecture	Native Qwen3.6
Training Method	Full Supervised Fine-Tuning
Training Corpus	Claude Mythos + Fable 5 Synthetic Corpus
Training Scale	105 Trillion Tokens
Checkpoint Type	Fully Fine-Tuned Model
LoRA	❌ No
MTP Layers	0
Precision	BF16
Framework	Transformers
Primary Domain	Reasoning, Coding, Technical Assistance

Training Philosophy

Qwable was developed around a single engineering principle:

Maximize practical reasoning quality rather than benchmark optimization.

Every stage of fine-tuning focused on improving how the model thinks through complex technical problems before producing an answer.

Training objectives included:

Stronger logical consistency
Better instruction adherence
Higher-quality code generation
Improved debugging capability
Superior architectural reasoning
More structured explanations
Reduced unnecessary verbosity
More deterministic outputs
Improved long-context coherence

Instead of generating longer responses, Qwable aims to generate better responses—clear, technically accurate, logically organized, and immediately actionable.

Why Full Fine-Tuning?

Qwable is distributed as a fully fine-tuned model, not an adapter.

This provides several practical advantages:

Native Hugging Face checkpoint
No adapter merging required
Simplified deployment pipelines
Better compatibility across inference engines
Easier downstream quantization
Straightforward GGUF conversion
Continued fine-tuning without additional merging
Production-ready distribution

Downloads last month: 528

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

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Model tree for JinglanWeb3/QwenFable

Base model

Qwen/Qwen3.6-27B

Finetuned

unsloth/Qwen3.6-27B

Quantized

(10)

this model

Duplicated from Mia-AiLab/Qwable-3.6-27b

JinglanWeb3
/

QwenFable

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Qwable 27B

Overview

Highlights

Model Specifications

Training Philosophy

Why Full Fine-Tuning?

Model tree for JinglanWeb3/QwenFable

Datasets used to train JinglanWeb3/QwenFable