Instructions to use toxzak/gemma4-e2b-exp-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toxzak/gemma4-e2b-exp-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="toxzak/gemma4-e2b-exp-quant")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("toxzak/gemma4-e2b-exp-quant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use toxzak/gemma4-e2b-exp-quant with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "toxzak/gemma4-e2b-exp-quant" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "toxzak/gemma4-e2b-exp-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/toxzak/gemma4-e2b-exp-quant
- SGLang
How to use toxzak/gemma4-e2b-exp-quant 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 "toxzak/gemma4-e2b-exp-quant" \ --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": "toxzak/gemma4-e2b-exp-quant", "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 "toxzak/gemma4-e2b-exp-quant" \ --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": "toxzak/gemma4-e2b-exp-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use toxzak/gemma4-e2b-exp-quant with Docker Model Runner:
docker model run hf.co/toxzak/gemma4-e2b-exp-quant
File size: 1,897 Bytes
9c41926 | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
from typing import Dict, Optional, List
from .Sub1BitLLM import Sub1BitLLM, Sub1BitConfig, from_fp16
from .lowrank_factorization import low_rank_factorize, factorize_model_weights, compute_optimal_rank
from .quantization import (
ternary_quantize, ternary_pack, ternary_unpack,
sigma_quantize, sigma_dequantize,
quantize_factor, pack_factor, unpack_factor, dequantize_factor,
)
from .groupwise_int4 import (
dequantize_groupwise_int4,
estimate_groupwise_int4_bpw,
pack_signed_int4,
quantize_groupwise_int4,
unpack_signed_int4,
)
from .mixed_budget import allocate_mixed_budget, summarize_allocation
from .error_budget_residual import (
dequantize_binary_residual,
dequantize_error_budget_residual,
estimate_binary_residual_bpw,
estimate_error_budget_residual_bpw,
quantize_binary_residual,
quantize_error_budget_residual,
)
from .gguf_writer import GGUFWriter, GGML_TYPES, GGUF_TYPES
from .pack_gguf import pack_sub1bit_model, QuantizedLayer
__all__ = [
"Sub1BitLLM",
"Sub1BitConfig",
"from_fp16",
"low_rank_factorize",
"factorize_model_weights",
"compute_optimal_rank",
"ternary_quantize",
"ternary_pack",
"ternary_unpack",
"sigma_quantize",
"sigma_dequantize",
"quantize_factor",
"pack_factor",
"unpack_factor",
"dequantize_factor",
"dequantize_groupwise_int4",
"estimate_groupwise_int4_bpw",
"pack_signed_int4",
"quantize_groupwise_int4",
"unpack_signed_int4",
"allocate_mixed_budget",
"summarize_allocation",
"dequantize_binary_residual",
"dequantize_error_budget_residual",
"estimate_binary_residual_bpw",
"estimate_error_budget_residual_bpw",
"quantize_binary_residual",
"quantize_error_budget_residual",
"GGUFWriter",
"GGML_TYPES",
"GGUF_TYPES",
"pack_sub1bit_model",
"QuantizedLayer",
]
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