Instructions to use Wizcoderr/qwen-flutter-fused with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wizcoderr/qwen-flutter-fused with Transformers:

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

pipe = pipeline("text-generation", model="Wizcoderr/qwen-flutter-fused")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wizcoderr/qwen-flutter-fused")
model = AutoModelForCausalLM.from_pretrained("Wizcoderr/qwen-flutter-fused", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use Wizcoderr/qwen-flutter-fused with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Wizcoderr/qwen-flutter-fused")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use Wizcoderr/qwen-flutter-fused with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Wizcoderr/qwen-flutter-fused

SGLang

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

How to use Wizcoderr/qwen-flutter-fused with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Wizcoderr/qwen-flutter-fused"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Wizcoderr/qwen-flutter-fused"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Wizcoderr/qwen-flutter-fused with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Wizcoderr/qwen-flutter-fused"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Wizcoderr/qwen-flutter-fused"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Wizcoderr/qwen-flutter-fused",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Wizcoderr/qwen-flutter-fused with Docker Model Runner:
```
docker model run hf.co/Wizcoderr/qwen-flutter-fused
```

Hermes Agent

How to use Wizcoderr/qwen-flutter-fused with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Wizcoderr/qwen-flutter-fused"

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 Wizcoderr/qwen-flutter-fused

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Wizcoderr/qwen-flutter-fused with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Wizcoderr/qwen-flutter-fused"

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 "Wizcoderr/qwen-flutter-fused" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Wizcoderr commited on May 23

Commit

0ff2428

verified ·

1 Parent(s): 433fb97

Upload folder using huggingface_hub

Browse files

Files changed (3) hide show

README.md +0 -3
chat_template.jinja +1 -1
config.json +1 -1

README.md CHANGED Viewed

@@ -4,7 +4,4 @@ tags:
 - mlx
 library_name: mlx
 pipeline_tag: text-generation
-license: apache-2.0
-base_model:
-- Qwen/Qwen2.5-Coder-14B-Instruct
 ---

 - mlx
 library_name: mlx
 pipeline_tag: text-generation
 ---

chat_template.jinja CHANGED Viewed

@@ -17,7 +17,7 @@
     {%- else %}
         {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
     {%- endif %}
-{%- endif %}
 {%- for message in messages %}
     {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
         {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}

     {%- else %}
         {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
     {%- endif %}
+{%- endif %}
 {%- for message in messages %}
     {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
         {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}

config.json CHANGED Viewed

@@ -9,7 +9,7 @@
     "hidden_size": 5120,
     "initializer_range": 0.02,
     "intermediate_size": 13824,
-    "max_position_embeddings": 32768,
     "max_window_layers": 70,
     "model_type": "qwen2",
     "num_attention_heads": 40,

     "hidden_size": 5120,
     "initializer_range": 0.02,
     "intermediate_size": 13824,
+    "max_position_embeddings": 128000,
     "max_window_layers": 70,
     "model_type": "qwen2",
     "num_attention_heads": 40,