Instructions to use abacusai/Giraffe-13b-32k-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Giraffe-13b-32k-v3 with Transformers:

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

pipe = pipeline("text-generation", model="abacusai/Giraffe-13b-32k-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abacusai/Giraffe-13b-32k-v3")
model = AutoModelForCausalLM.from_pretrained("abacusai/Giraffe-13b-32k-v3")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abacusai/Giraffe-13b-32k-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacusai/Giraffe-13b-32k-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Giraffe-13b-32k-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abacusai/Giraffe-13b-32k-v3

SGLang

How to use abacusai/Giraffe-13b-32k-v3 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 "abacusai/Giraffe-13b-32k-v3" \
    --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": "abacusai/Giraffe-13b-32k-v3",
		"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 "abacusai/Giraffe-13b-32k-v3" \
        --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": "abacusai/Giraffe-13b-32k-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abacusai/Giraffe-13b-32k-v3 with Docker Model Runner:
```
docker model run hf.co/abacusai/Giraffe-13b-32k-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Llama2 13B finetuned to process longer (32K) contexts using interpolation and then further instruct finetuned with ShareGPT and Orca-Chat.

Model Details

Model Description

This is next version of the model trained and evaluated as part of the experiments described in the repo http://github.com/abacusai/Long-Context. This version was trained with a scaling factor of 8 and shows better reasoning and math abilites on State of the Art benchmarks

Developed by: Abacus.AI
Model type: Transformer based autoregressive causal language model
License: Llama 2 Community License: https://github.com/facebookresearch/llama/blob/main/LICENSE
Finetuned from model: Llama V2 13B

Model Sources [optional]

Repository: http://github.com/abacusai/Long-Context

Direct Use

Since the model is instruct finetuned it can also be directly used for various prompted tasks. We have tested it on open book question answering using the long context to supply search results.

Bias, Risks, and Limitations

The model has not been evaluated for safety and is only intended for research and experiments.

Downloads last month: 201

Safetensors

Model size

13B params

Tensor type

F16

Model tree for abacusai/Giraffe-13b-32k-v3

Quantizations

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