Instructions to use SlayerLab/fabryka-english-250m-e01-sft-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlayerLab/fabryka-english-250m-e01-sft-v1 with Transformers:

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

pipe = pipeline("text-generation", model="SlayerLab/fabryka-english-250m-e01-sft-v1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SlayerLab/fabryka-english-250m-e01-sft-v1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SlayerLab/fabryka-english-250m-e01-sft-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SlayerLab/fabryka-english-250m-e01-sft-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlayerLab/fabryka-english-250m-e01-sft-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SlayerLab/fabryka-english-250m-e01-sft-v1

SGLang

How to use SlayerLab/fabryka-english-250m-e01-sft-v1 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 "SlayerLab/fabryka-english-250m-e01-sft-v1" \
    --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": "SlayerLab/fabryka-english-250m-e01-sft-v1",
		"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 "SlayerLab/fabryka-english-250m-e01-sft-v1" \
        --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": "SlayerLab/fabryka-english-250m-e01-sft-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SlayerLab/fabryka-english-250m-e01-sft-v1 with Docker Model Runner:
```
docker model run hf.co/SlayerLab/fabryka-english-250m-e01-sft-v1
```

fabryka-english-250m-e01-sft-v1 / evaluation /checkpoint.json

kacperwikiel

Release experimental E01 SFT with verified before-after benchmarks

908ce20 verified 7 days ago

Raw

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	{
	"complete": true,
	"updates": 509,
	"assistant_targets": 2499600,
	"sha256": {
	"checkpoint.pt": "01a8d6ee93ac84cc46bf1041b4f464d2674d13e6a7e66663eb636978126ab77b",
	"export/model.safetensors": "ddc598302ef545e41db513e35df40935bb0bcf03c7a8947cc99214fefa9b5ca6",
	"result.json": "ec2d4d40c6d3805df2b14253cac87c38623e5885b346d648261bdab7ab965134",
	"events.jsonl": "5055e8a1cfd25641f7f1d8f86f4f21bb02609f1798ba9890c417397300cbe061"
	},
	"off_host_hashes_verified": true,
	"export_tensors_equal_final_checkpoint": true,
	"export_tensors": 290,
	"local_backup": "/root/data/rfc005/sft-e01-code-v1/backup"
	}