Text Generation
Transformers
Safetensors
MLX
qwen3_5
image-text-to-text
text-generation-inference
unsloth
qwen3_6
reasoning
chain-of-thought
lora
sft
agent
tool-use
function-calling
coder
conversational
6-bit
Instructions to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit") model = AutoModelForMultimodalLM.from_pretrained("Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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("Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit") 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 Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit
- SGLang
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" \ --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": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", "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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" \ --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": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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 Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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 Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", max_seq_length=2048, ) - Pi
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with Docker Model Runner:
docker model run hf.co/Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit
- Hermes Agent
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
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 Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" \ --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"
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{%- if tools %}
{{- '<|im_start|>system
' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '
' }}
{%- endif %}
{{- "# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>" }}
{%- for tool in tools %}
{{- "
" }}
{{- tool | tojson }}
{%- endfor %}
{{- "
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{\"name\": <function-name>, \"arguments\": <args-json-object>}
</tool_call><|im_end|>
" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system
' + messages[0].content + '<|im_end|>
' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
{%- set current_content = message.content if message.content is not none else '' %}
{%- set tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = current_content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (current_content|length) - tool_end_length %}
{%- if start_pos < 0 %}
{%- set start_pos = 0 %}
{%- endif %}
{%- set end_of_message = current_content[start_pos:] %}
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '
' + message.content + '<|im_end|>' + '
' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = (message.content.split('</think>')|last).lstrip('
') %}
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('
') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('
') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '
<think>
' + reasoning_content.strip('
') + '
</think>
' + content.lstrip('
') }}
{%- else %}
{{- '<|im_start|>' + message.role + '
' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '
' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '
' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>
{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}
</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>
' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '
<tool_response>
' }}
{{- message.content }}
{{- '
</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>
' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant
' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>
</think>
' }}
{%- endif %}
{%- endif %}
|