Instructions to use archit11/qwen_worldmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use archit11/qwen_worldmodel with Transformers:

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

pipe = pipeline("text-generation", model="archit11/qwen_worldmodel")

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

tokenizer = AutoTokenizer.from_pretrained("archit11/qwen_worldmodel")
model = AutoModelForCausalLM.from_pretrained("archit11/qwen_worldmodel", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use archit11/qwen_worldmodel 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 archit11/qwen_worldmodel
# Run inference directly in the terminal:
llama cli -hf archit11/qwen_worldmodel

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf archit11/qwen_worldmodel
# Run inference directly in the terminal:
llama cli -hf archit11/qwen_worldmodel

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 archit11/qwen_worldmodel
# Run inference directly in the terminal:
./llama-cli -hf archit11/qwen_worldmodel

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 archit11/qwen_worldmodel
# Run inference directly in the terminal:
./build/bin/llama-cli -hf archit11/qwen_worldmodel

Use Docker

docker model run hf.co/archit11/qwen_worldmodel

LM Studio
Jan

vLLM

How to use archit11/qwen_worldmodel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "archit11/qwen_worldmodel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "archit11/qwen_worldmodel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/archit11/qwen_worldmodel

SGLang

How to use archit11/qwen_worldmodel 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 "archit11/qwen_worldmodel" \
    --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": "archit11/qwen_worldmodel",
		"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 "archit11/qwen_worldmodel" \
        --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": "archit11/qwen_worldmodel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use archit11/qwen_worldmodel with Ollama:
```
ollama run hf.co/archit11/qwen_worldmodel
```
Unsloth Desktop

How to use archit11/qwen_worldmodel with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf archit11/qwen_worldmodel

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "archit11/qwen_worldmodel"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use archit11/qwen_worldmodel with Docker Model Runner:
```
docker model run hf.co/archit11/qwen_worldmodel
```

Lemonade

How to use archit11/qwen_worldmodel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull archit11/qwen_worldmodel

Run and chat with the model

lemonade run user.qwen_worldmodel-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use archit11/qwen_worldmodel with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf archit11/qwen_worldmodel

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 archit11/qwen_worldmodel

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use archit11/qwen_worldmodel with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf archit11/qwen_worldmodel

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 "archit11/qwen_worldmodel" \
  --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"

Improve language tag

by lbourdois - opened Apr 27, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+97

-83

Files changed (1) hide show

README.md +97 -83

README.md CHANGED Viewed

@@ -1,84 +1,98 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: Qwen/Qwen2.5-0.5B
-tags:
-- generated_from_trainer
-- qwen
-- GGUF
-- worldmodel
-- worldbuilding
-model-index:
-- name: capybara_finetuned_results3
-  results: []
-datasets:
-- archit11/worldbuilding
----
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-# capybara_finetuned_results3
-This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 5.6542
-## video demo : (its pretty bad)
-<video controls autoplay muted src="https://0x0.st/XgZs.mp4"></video>
-More information needed
-## Intended uses & limitations
-More information needed
-## Training and evaluation data
-More information needed
-## Training procedure
-### Training hyperparameters
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 1
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 4
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 5
-- training_steps: 800
-### Training results
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 15.5311       | 0.0230 | 50   | 14.5422         |
-| 8.7477        | 0.0460 | 100  | 9.2952          |
-| 7.3554        | 0.0690 | 150  | 7.1992          |
-| 6.828         | 0.0920 | 200  | 6.7258          |
-| 6.4694        | 0.1150 | 250  | 6.3597          |
-| 6.3401        | 0.1381 | 300  | 6.1703          |
-| 6.1256        | 0.1611 | 350  | 6.0395          |
-| 6.0372        | 0.1841 | 400  | 5.9271          |
-| 6.0221        | 0.2071 | 450  | 5.8464          |
-| 5.8783        | 0.2301 | 500  | 5.7810          |
-| 5.8339        | 0.2531 | 550  | 5.7335          |
-| 5.8546        | 0.2761 | 600  | 5.6904          |
-| 5.9169        | 0.2991 | 650  | 5.6690          |
-| 5.7959        | 0.3221 | 700  | 5.6565          |
-| 5.7271        | 0.3451 | 750  | 5.6543          |
-| 5.8734        | 0.3682 | 800  | 5.6542          |
-### Framework versions
-- Transformers 4.44.2
-- Pytorch 2.4.0
-- Datasets 3.0.0
 - Tokenizers 0.19.1

+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B
+tags:
+- generated_from_trainer
+- qwen
+- GGUF
+- worldmodel
+- worldbuilding
+datasets:
+- archit11/worldbuilding
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+model-index:
+- name: capybara_finetuned_results3
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# capybara_finetuned_results3
+This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 5.6542
+## video demo : (its pretty bad)
+<video controls autoplay muted src="https://0x0.st/XgZs.mp4"></video>
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 5
+- training_steps: 800
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 15.5311       | 0.0230 | 50   | 14.5422         |
+| 8.7477        | 0.0460 | 100  | 9.2952          |
+| 7.3554        | 0.0690 | 150  | 7.1992          |
+| 6.828         | 0.0920 | 200  | 6.7258          |
+| 6.4694        | 0.1150 | 250  | 6.3597          |
+| 6.3401        | 0.1381 | 300  | 6.1703          |
+| 6.1256        | 0.1611 | 350  | 6.0395          |
+| 6.0372        | 0.1841 | 400  | 5.9271          |
+| 6.0221        | 0.2071 | 450  | 5.8464          |
+| 5.8783        | 0.2301 | 500  | 5.7810          |
+| 5.8339        | 0.2531 | 550  | 5.7335          |
+| 5.8546        | 0.2761 | 600  | 5.6904          |
+| 5.9169        | 0.2991 | 650  | 5.6690          |
+| 5.7959        | 0.3221 | 700  | 5.6565          |
+| 5.7271        | 0.3451 | 750  | 5.6543          |
+| 5.8734        | 0.3682 | 800  | 5.6542          |
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.4.0
+- Datasets 3.0.0
 - Tokenizers 0.19.1