Text Generation
Transformers
Safetensors
English
fabryka_english_base
experimental
base-model
custom-code
custom_code
Instructions to use SlayerLab/fabryka-english-base-250m-e01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlayerLab/fabryka-english-base-250m-e01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SlayerLab/fabryka-english-base-250m-e01", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SlayerLab/fabryka-english-base-250m-e01", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SlayerLab/fabryka-english-base-250m-e01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SlayerLab/fabryka-english-base-250m-e01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SlayerLab/fabryka-english-base-250m-e01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SlayerLab/fabryka-english-base-250m-e01
- SGLang
How to use SlayerLab/fabryka-english-base-250m-e01 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-base-250m-e01" \ --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": "SlayerLab/fabryka-english-base-250m-e01", "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 "SlayerLab/fabryka-english-base-250m-e01" \ --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": "SlayerLab/fabryka-english-base-250m-e01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SlayerLab/fabryka-english-base-250m-e01 with Docker Model Runner:
docker model run hf.co/SlayerLab/fabryka-english-base-250m-e01
Download evaluation/instruct-base-e01.json from SlayerLab/fabryka-english-base-250m-e01: direct link, hf CLI and curl.
- Browser
- Download file 4.2 kB
-
https://huggingface.co/SlayerLab/fabryka-english-base-250m-e01/resolve/main/evaluation/instruct-base-e01.json
- Command line
-
hf download hf://SlayerLab/fabryka-english-base-250m-e01/evaluation/instruct-base-e01.json
-
curl -L -o instruct-base-e01.json https://huggingface.co/SlayerLab/fabryka-english-base-250m-e01/resolve/main/evaluation/instruct-base-e01.json
4.2 kB
| { | |
| "status": "verified", | |
| "model": "SlayerLab/fabryka-english-base-250m-e01", | |
| "model_weights_sha256": "7e3d9655bde8b62b72fece4d715ce51c99ab09c57be9aff322e187fa46e62ae9", | |
| "benchmark": "BananaMind Instruct Bench 1.1", | |
| "dataset_id": "BananaMind/BananaMind-Instruct-Bench-1.1", | |
| "dataset_revision": "40494cb4a9224bfd78722968efd2bff440e08186", | |
| "dataset_sha256": "2369407245d2d440b0be991009d0d1f28a97fd3df5c8fba8e806cbe9f31d4eb1", | |
| "runner_sha256": "87cae0182a190da421c796cddd484ecfef630446b43b6b285bf178e4abf5d8ab", | |
| "private_report_sha256": "4e16e8be49c9c3dce86bfce44dbd24e32937c025db7f3425714203c8f53c825f", | |
| "device": "cuda:0", | |
| "dtype": "bfloat16", | |
| "prompt_format": "alpaca_instruction_fallback", | |
| "generation_settings": { | |
| "do_sample": false, | |
| "repetition_penalty": 1.1, | |
| "use_cache": true, | |
| "seed": 42 | |
| }, | |
| "summary": { | |
| "cases": 300, | |
| "passed": 1, | |
| "pass_rate": 0.0033333333333333335, | |
| "weighted_points": 2.0250000000000004, | |
| "possible_weighted_points": 573.5625, | |
| "weighted_score": 0.003530565544295522, | |
| "official_complete_run": true, | |
| "overall_elo": 163, | |
| "overall_elo_unrounded": 162.9586801157697, | |
| "categories": { | |
| "general": { | |
| "cases": 120, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 190.0, | |
| "weighted_score": 0.0, | |
| "elo": 129, | |
| "elo_unrounded": 129.29686854727566 | |
| }, | |
| "multi_turn": { | |
| "cases": 75, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 148.4375, | |
| "weighted_score": 0.0, | |
| "elo": 318, | |
| "elo_unrounded": 318.23569578211266 | |
| }, | |
| "system_prompts": { | |
| "cases": 60, | |
| "passed": 1, | |
| "pass_rate": 0.016666666666666666, | |
| "weighted_points": 2.0250000000000004, | |
| "possible_weighted_points": 128.25, | |
| "weighted_score": 0.01578947368421053, | |
| "elo": 516, | |
| "elo_unrounded": 515.6348921626286 | |
| }, | |
| "recall_in_context": { | |
| "cases": 30, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 68.875, | |
| "weighted_score": 0.0, | |
| "elo": 537, | |
| "elo_unrounded": 537.1739454704327 | |
| }, | |
| "code": { | |
| "cases": 15, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 38.0, | |
| "weighted_score": 0.0, | |
| "elo": 667, | |
| "elo_unrounded": 666.6862941801066 | |
| } | |
| }, | |
| "difficulties": { | |
| "easy": { | |
| "cases": 100, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 120.75, | |
| "weighted_score": 0.0, | |
| "elo": 192, | |
| "elo_unrounded": 191.59059734804697 | |
| }, | |
| "medium": { | |
| "cases": 100, | |
| "passed": 1, | |
| "pass_rate": 0.01, | |
| "weighted_points": 2.0250000000000004, | |
| "possible_weighted_points": 181.125, | |
| "weighted_score": 0.011180124223602487, | |
| "elo": 343, | |
| "elo_unrounded": 343.3623736269484 | |
| }, | |
| "hard": { | |
| "cases": 100, | |
| "passed": 0, | |
| "pass_rate": 0.0, | |
| "weighted_points": 0.0, | |
| "possible_weighted_points": 271.6875, | |
| "weighted_score": 0.0, | |
| "elo": 296, | |
| "elo_unrounded": 295.84042114108263 | |
| } | |
| }, | |
| "natural_eos_count": 51, | |
| "generation_limit_hits": 249, | |
| "context_overflows": 0 | |
| }, | |
| "verification": "All 300 ordered IDs, messages, item metadata, deterministic pass judgments, weights and aggregates recomputed against the pinned dataset and runner. No inference rerun.", | |
| "overlap_caveat": "Instruct access was obtained after pretraining. This verification does not establish pretraining-set decontamination against Instruct Bench.", | |
| "limitations": [ | |
| "Code checks use syntax and patterns, not program execution.", | |
| "Comparison model scores are publisher self-reports, not a local rerun.", | |
| "Base uses the official Alpaca fallback; SFT uses native chat. A before/after comparison includes prompt-format differences." | |
| ] | |
| } | |