Instructions to use acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 with Transformers:

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

pipe = pipeline("text-generation", model="acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1")
model = AutoModelForCausalLM.from_pretrained("acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1

SGLang

How to use acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 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 "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1" \
    --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": "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1",
		"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 "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1" \
        --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": "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 with Docker Model Runner:
```
docker model run hf.co/acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is an experimental merge of models RedPajama-INCITE-Chat-3B-V1 and RedPajama-INCITE-Instruct-3B-V1.
This model is adaptive to prompt templates, but this template is recommended:

HUMAN: {prompt}
ASSISTANT:

Feel free to change HUMAN or ASSISTANT. It will not change much.
GGML versions here (Note that this is only compatible with koboldcpp).

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	39.23
ARC (25-shot)	42.58
HellaSwag (10-shot)	67.48
MMLU (5-shot)	25.99
TruthfulQA (0-shot)	33.62
Winogrande (5-shot)	64.8
GSM8K (5-shot)	0.91

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	39.23
AI2 Reasoning Challenge (25-Shot)	42.58
HellaSwag (10-Shot)	67.48
MMLU (5-Shot)	25.99
TruthfulQA (0-shot)	33.62
Winogrande (5-shot)	64.80
GSM8k (5-shot)	0.91

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1

Quantizations

3 models

Datasets used to train acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1

Spaces using acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1 31

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

42.580
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

67.480
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

25.990
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

33.620
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

64.800
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.910