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)

# pip install -U transformers accelerate
# 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 / fabric_config.json

tudor-iustin22

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	{
	"activation_checkpointing": true,
	"architecture": "fabric",
	"attention_backend": "auto",
	"attention_chunk_size": 1024,
	"checkpoint_dtype": "float16",
	"checkpoint_note": "Model-only Fabric complete checkpoint; optimizer state is excluded.",
	"checkpoint_sha256": "a8d12a687fe5eaa05b601ab2803ca2c14b9cc2c14d754110ca4569b061af2bf1",
	"checkpoint_size_gib": 1.383114,
	"chunked_cross_entropy": true,
	"continued_pretraining_tokens": 5000134656,
	"cumulative_pretraining_tokens": 23000514560,
	"export_kind": "model_only_inference_fp16",
	"head_dim": 64,
	"hidden_size": 1536,
	"intermediate_size": 4096,
	"license": "fabric-ai-open-1.0",
	"local_attention_window": 2048,
	"loss_chunk_size": 1024,
	"memory_chunk_size": 512,
	"model_name": "Fabric1.5-0.7B-Instruct",
	"num_kv_heads": 6,
	"num_layers": 24,
	"num_parameters": 742528520,
	"num_query_heads": 24,
	"original_pretraining_tokens": 18000379904,
	"posttraining_supervised_tokens": 318879711,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"sequence_length": 32768,
	"summaries_per_chunk": 4,
	"tie_word_embeddings": true,
	"torch_dtype": "float16",
	"vocab_size": 65536
	}