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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

llama-cpp-python

How to use archit11/qwen_worldmodel with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="archit11/qwen_worldmodel",
	filename="qwen_worldmodel0.1.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

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 Studio

How to use archit11/qwen_worldmodel 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 archit11/qwen_worldmodel 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 archit11/qwen_worldmodel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for archit11/qwen_worldmodel to start chatting

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 @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": "archit11/qwen_worldmodel"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

archit11 commited on Sep 28, 2024

Commit

bf44ba9

verified ·

1 Parent(s): e4d4d49

archit11/qwen-finetuned-model

Browse files

Files changed (6) hide show

README.md +76 -0
config.json +29 -0
generation_config.json +6 -0
model.safetensors +3 -0
runs/Sep28_10-42-26_e89ab5377562/events.out.tfevents.1727520148.e89ab5377562.24.0 +3 -0
training_args.bin +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,76 @@

+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B
+tags:
+- generated_from_trainer
+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
+## Model description
+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

config.json ADDED Viewed

	@@ -0,0 +1,29 @@

+{
+  "_name_or_path": "Qwen/Qwen2.5-0.5B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,6 @@

+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.44.2"
+}

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