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: 1,628 Bytes

ea1882d

{
  "activation_checkpointing": true,
  "architecture": "fabric",
  "architectures": [
    "FabricForCausalLM"
  ],
  "attention_backend": "auto",
  "attention_chunk_size": 1024,
  "auto_map": {
    "AutoConfig": "configuration_fabric.FabricConfig",
    "AutoModelForCausalLM": "modeling_fabric.FabricForCausalLM"
  },
  "bos_token_id": 2,
  "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,
  "eos_token_id": 7,
  "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,
  "max_position_embeddings": 32768,
  "memory_chunk_size": 512,
  "model_name": "Fabric1.5-0.7B-Instruct",
  "model_type": "fabric",
  "num_attention_heads": 24,
  "num_hidden_layers": 24,
  "num_key_value_heads": 6,
  "num_kv_heads": 6,
  "num_layers": 24,
  "num_parameters": 742528520,
  "num_query_heads": 24,
  "original_pretraining_tokens": 18000379904,
  "pad_token_id": 0,
  "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",
  "transformers_version": "custom",
  "use_cache": false,
  "vocab_size": 65536
}