Text Generation
Transformers
Safetensors
MLX
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
gemma3_text
conversational
instruct
experimental
mlx-my-repo
text-generation-inference
🇪🇺 Region: EU
Instructions to use NbAiLab/borealis-270m-instruct-preview-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/borealis-270m-instruct-preview-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NbAiLab/borealis-270m-instruct-preview-mlx") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/borealis-270m-instruct-preview-mlx") model = AutoModelForCausalLM.from_pretrained("NbAiLab/borealis-270m-instruct-preview-mlx") 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 NbAiLab/borealis-270m-instruct-preview-mlx 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("NbAiLab/borealis-270m-instruct-preview-mlx") 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 NbAiLab/borealis-270m-instruct-preview-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NbAiLab/borealis-270m-instruct-preview-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-270m-instruct-preview-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NbAiLab/borealis-270m-instruct-preview-mlx
- SGLang
How to use NbAiLab/borealis-270m-instruct-preview-mlx 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 "NbAiLab/borealis-270m-instruct-preview-mlx" \ --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": "NbAiLab/borealis-270m-instruct-preview-mlx", "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 "NbAiLab/borealis-270m-instruct-preview-mlx" \ --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": "NbAiLab/borealis-270m-instruct-preview-mlx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use NbAiLab/borealis-270m-instruct-preview-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "NbAiLab/borealis-270m-instruct-preview-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "NbAiLab/borealis-270m-instruct-preview-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-270m-instruct-preview-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use NbAiLab/borealis-270m-instruct-preview-mlx with Docker Model Runner:
docker model run hf.co/NbAiLab/borealis-270m-instruct-preview-mlx
Upload chat_template.jinja with huggingface_hub
Browse files- chat_template.jinja +47 -0
chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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