Instructions to use FabricAI/Fabric1.5-0.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FabricAI/Fabric1.5-0.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="FabricAI/Fabric1.5-0.7B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FabricAI/Fabric1.5-0.7B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FabricAI/Fabric1.5-0.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FabricAI/Fabric1.5-0.7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FabricAI/Fabric1.5-0.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FabricAI/Fabric1.5-0.7B-Instruct

SGLang

How to use FabricAI/Fabric1.5-0.7B-Instruct 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 "FabricAI/Fabric1.5-0.7B-Instruct" \
    --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": "FabricAI/Fabric1.5-0.7B-Instruct",
		"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 "FabricAI/Fabric1.5-0.7B-Instruct" \
        --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": "FabricAI/Fabric1.5-0.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FabricAI/Fabric1.5-0.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/FabricAI/Fabric1.5-0.7B-Instruct
```

Fabric1.5-0.7B-Instruct

File size: 1,135 Bytes

ea1882d

{
  "additional_special_tokens": [
    "<|system|>",
    "<|user|>",
    "<|assistant|>"
  ],
  "bos_token": "<bos>",
  "chat_template": "{% set default_system_prompt = \"You are Fabric, a large language model developed by Fabric AI.\" %}{% set ns = namespace(has_system=false) %}{% for message in messages %}{% if message['role'] == 'system' %}{% set ns.has_system = true %}{% endif %}{% endfor %}{% if not ns.has_system %}<|system|>\n{{ default_system_prompt }}<|end|>\n{% endif %}{% for message in messages %}{% if message['role'] == 'system' %}<|system|>\n{{ message['content'] }}<|end|>\n{% elif message['role'] == 'user' %}<|user|>\n{{ message['content'] }}<|end|>\n{% elif message['role'] == 'assistant' %}<|assistant|>\n{{ message['content'] }}<|end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>\n{% endif %}",
  "chat_template_file": "chat_template.jinja",
  "default_system_prompt": "You are Fabric, a large language model developed by Fabric AI.",
  "eos_token": "<|end|>",
  "model_max_length": 32768,
  "pad_token": "<pad>",
  "tokenizer_class": "PreTrainedTokenizerFast",
  "unk_token": "<unk>"
}