Instructions to use keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML with Transformers:

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

pipe = pipeline("text-generation", model="keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML")
model = AutoModelForCausalLM.from_pretrained("keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML

SGLang

How to use keldenl/RedPajama-INCITE-Instruct-3B-v1-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 "keldenl/RedPajama-INCITE-Instruct-3B-v1-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": "keldenl/RedPajama-INCITE-Instruct-3B-v1-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 "keldenl/RedPajama-INCITE-Instruct-3B-v1-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": "keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML with Docker Model Runner:
```
docker model run hf.co/keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML
```

Original Model Link: https://huggingface.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1

This will NOT work with llama.cpp as of 5/13/2023, but this NOW works (5/13/2023) with the GGML in https://github.com/ggerganov/ggml/ via gpt-neox This also works in my project https://github.com/keldenl/gpt-llama.cpp (uses ggml as an InferenceEngine).

RedPajama-INCITE-Instruct-3B-v1

RedPajama-INCITE-Instruct-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.

The model was fine-tuned for few-shot applications on the data of GPT-JT, with exclusion of tasks that overlap with the HELM core scenarios.

Model Details

Developed by: Together Computer.
Model type: Language Model
Language(s): English
License: Apache 2.0
Model Description: A 2.8B parameter pretrained language model.

Prompt Template

To prompt the chat model, use a typical instruction format + few shot prompting, for example:

Paraphrase the given sentence into a different sentence.

Input: Can you recommend some upscale restaurants in New York?
Output: What upscale restaurants do you recommend in New York?

Input: What are the famous places we should not miss in Paris?
Output: Recommend some of the best places to visit in Paris?

Input: Could you recommend some hotels that have cheap price in Zurich?
Output:

Which model to download?

The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
The q5_1 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_1.

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RedPajama-INCITE-Instruct-3B-v1-GGML

RedPajama-INCITE-Instruct-3B-v1

Model Details

Prompt Template

Which model to download?

Datasets used to train keldenl/RedPajama-INCITE-Instruct-3B-v1-GGML