Text Generation
Transformers
Safetensors
llama
grimboost
speculative-decoding
inference-optimization
confidence-gated
cascading-re-evaluation
local-inference
causal-lm
research
conversational
text-generation-inference
Instructions to use Grimlabs/GrimBoost-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Grimlabs/GrimBoost-01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Grimlabs/GrimBoost-01") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Grimlabs/GrimBoost-01") model = AutoModelForCausalLM.from_pretrained("Grimlabs/GrimBoost-01", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Grimlabs/GrimBoost-01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Grimlabs/GrimBoost-01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Grimlabs/GrimBoost-01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Grimlabs/GrimBoost-01
- SGLang
How to use Grimlabs/GrimBoost-01 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 "Grimlabs/GrimBoost-01" \ --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": "Grimlabs/GrimBoost-01", "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 "Grimlabs/GrimBoost-01" \ --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": "Grimlabs/GrimBoost-01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Grimlabs/GrimBoost-01 with Docker Model Runner:
docker model run hf.co/Grimlabs/GrimBoost-01
File size: 1,445 Bytes
60e7bb5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | from types import SimpleNamespace
import pytest
from grimboost.targets.hf import HFTarget
@pytest.mark.parametrize('mode,budget,enabled', [
('auto', 256, False), ('auto', 8191, False), ('auto', 8192, True),
('off', 50000, False), ('on', 256, True),
])
def test_optimization_dispatch_uses_requested_generation_budget(mode, budget, enabled):
calls = []
target = HFTarget.__new__(HFTarget)
target.decode_optimizations = mode
target.decode_optimizations_fallback = None
target._decode = SimpleNamespace(enable_optimizations=lambda: calls.append(True))
target.prepare_generation(budget)
assert bool(calls) is enabled
def test_auto_optimization_falls_back_once_on_unsupported_configuration():
calls = []
def unsupported():
calls.append(True)
raise ValueError('unsupported quantization kernel')
target = HFTarget.__new__(HFTarget)
target.decode_optimizations = 'auto'
target.decode_optimizations_fallback = None
target._decode = SimpleNamespace(enable_optimizations=unsupported)
target.prepare_generation(50000)
target.prepare_generation(50000)
assert calls == [True]
assert target.decode_optimizations_fallback == 'unsupported quantization kernel'
target.decode_optimizations = 'on'
target.decode_optimizations_fallback = None
with pytest.raises(ValueError, match='unsupported quantization kernel'):
target.prepare_generation(50000)
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