Instructions to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit") model = AutoModelForCausalLM.from_pretrained("ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit") 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 ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit 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("ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit") 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
- LM Studio
- vLLM
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit
- SGLang
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit 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 "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit" \ --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": "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit", "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 "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit" \ --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": "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi new
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit"
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": "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit 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 "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit"
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 ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit
Run Hermes
hermes
- MLX LM
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit with Docker Model Runner:
docker model run hf.co/ssdataanalysis/AI21-Jamba2-Mini-mlx-8Bit
| {%- if bos_token is defined and bos_token is not none %}{{- bos_token -}}{%- endif %} | |
| {%- if tools %} | |
| {{- '<|im_start|>system\n' }} | |
| {%- if messages|length > 0 and messages[0].role == 'system' %} | |
| {{- messages[0].content + '\n\n' }} | |
| {%- endif %} | |
| {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }} | |
| {%- for tool in tools %} | |
| {{- "\n" }} | |
| {{- tool | tojson }} | |
| {%- endfor %} | |
| {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }} | |
| {%- else %} | |
| {%- if messages|length > 0 and messages[0].role == 'system' %} | |
| {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} | |
| {%- for message in messages[::-1] %} | |
| {%- set index = (messages|length - 1) - loop.index0 %} | |
| {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %} | |
| {%- 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 + '\n' + message.content + '<|im_end|>' + '\n' }} | |
| {%- elif message.role == "assistant" %} | |
| {%- set content = message.content %} | |
| {{- '<|im_start|>' + message.role + '\n' + content }} | |
| {%- if message.tool_calls %} | |
| {%- for tool_call in message.tool_calls %} | |
| {%- if (loop.first and content) or (not loop.first) %} | |
| {{- '\n' }} | |
| {%- endif %} | |
| {%- if tool_call.function %} | |
| {%- set tool_call = tool_call.function %} | |
| {%- endif %} | |
| {{- '<tool_call>\n{"name": "' }} | |
| {{- tool_call.name }} | |
| {{- '", "arguments": ' }} | |
| {%- if tool_call.arguments is string %} | |
| {{- tool_call.arguments }} | |
| {%- else %} | |
| {{- tool_call.arguments | tojson }} | |
| {%- endif %} | |
| {{- '}\n</tool_call>' }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- '<|im_end|>\n' }} | |
| {%- elif message.role == "tool" %} | |
| {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} | |
| {{- '<|im_start|>user' }} | |
| {%- endif %} | |
| {{- '\n<tool_response>\n' }} | |
| {{- message.content }} | |
| {{- '\n</tool_response>' }} | |
| {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} | |
| {{- '<|im_end|>\n' }} | |
| {%- endif %} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {{- '<|im_start|>assistant\n' }} | |
| {%- endif -%} | |