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

Libraries

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

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

pipe = pipeline("text-generation", model="FabricAI/Fabric1.5-0.7B-Base", trust_remote_code=True)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FabricAI/Fabric1.5-0.7B-Base 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-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FabricAI/Fabric1.5-0.7B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use FabricAI/Fabric1.5-0.7B-Base 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-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FabricAI/Fabric1.5-0.7B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FabricAI/Fabric1.5-0.7B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Fabric1.5-0.7B-Base / config.json

tudor-iustin22

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	{
	"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": "62a50732d1b1f4f94ba3c4c9838b42777f773fe038126cb6307b235bdfc9878a",
	"checkpoint_size_gib": 1.383114,
	"chunked_cross_entropy": true,
	"continued_pretraining_tokens": 5000134656,
	"cumulative_pretraining_tokens": 23000514560,
	"eos_token_id": 3,
	"export_kind": "model_only_inference_fp16",
	"head_dim": 64,
	"hidden_size": 1536,
	"intermediate_size": 4096,
	"license": "apache-2.0",
	"local_attention_window": 2048,
	"loss_chunk_size": 1024,
	"max_position_embeddings": 32768,
	"memory_chunk_size": 512,
	"model_name": "Fabric1.5-0.7B-Base",
	"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": 0,
	"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
	}