Instructions to use rustformers/gpt-j-ggml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rustformers/gpt-j-ggml with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rustformers/gpt-j-ggml")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rustformers/gpt-j-ggml", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use rustformers/gpt-j-ggml with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rustformers/gpt-j-ggml" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rustformers/gpt-j-ggml", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rustformers/gpt-j-ggml
- SGLang
How to use rustformers/gpt-j-ggml 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 "rustformers/gpt-j-ggml" \ --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": "rustformers/gpt-j-ggml", "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 "rustformers/gpt-j-ggml" \ --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": "rustformers/gpt-j-ggml", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rustformers/gpt-j-ggml with Docker Model Runner:
docker model run hf.co/rustformers/gpt-j-ggml
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rustformers/gpt-j-ggml", dtype="auto")GGML converted versions of EleutherAI's GPT-J model
Description
GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
| Hyperparameter | Value |
|---|---|
| 6053381344 | |
| 28* | |
| 4096 | |
| 16384 | |
| 16 | |
| 256 | |
| 2048 | |
| 50257/50400β (same tokenizer as GPT-2/3) | |
| Positional Encoding | Rotary Position Embedding (RoPE) |
| RoPE Dimensions | 64 |
* Each layer consists of one feedforward block and one self attention block.
β Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3.
Converted Models
| Name | Based on | Type | Container | GGML Version |
|---|---|---|---|---|
| gpt-j-6b-f16.bin | EleutherAI/gpt-j-6b | F16 | GGML | V3 |
| gpt-j-6b-q4_0.bin | EleutherAI/gpt-j-6b | Q4_0 | GGML | V3 |
| gpt-j-6b-q4_0-ggjt.bin | EleutherAI/gpt-j-6b | Q4_0 | GGJT | V3 |
| gpt-j-6b-q5_1.bin | EleutherAI/gpt-j-6b | Q5_1 | GGML | V3 |
| gpt-j-6b-q5_1-ggjt.bin | EleutherAI/gpt-j-6b | Q5_1 | GGJT | V3 |
Usage
Python via llm-rs:
Installation
Via pip: pip install llm-rs
Run inference
from llm_rs import AutoModel
#Load the model, define any model you like from the list above as the `model_file`
model = AutoModel.from_pretrained("rustformers/gpt-j-ggml",model_file="gpt-j-6b-q4_0-ggjt.bin")
#Generate
print(model.generate("The meaning of life is"))
Rust via Rustformers/llm:
Installation
git clone --recurse-submodules https://github.com/rustformers/llm.git
cd llm
cargo build --release
Run inference
cargo run --release -- gptj infer -m path/to/model.bin -p "Tell me how cool the Rust programming language is:"
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rustformers/gpt-j-ggml")