Instructions to use syzymon/long_llama_code_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use syzymon/long_llama_code_7b with Transformers:

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

pipe = pipeline("text-generation", model="syzymon/long_llama_code_7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("syzymon/long_llama_code_7b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use syzymon/long_llama_code_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "syzymon/long_llama_code_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "syzymon/long_llama_code_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/syzymon/long_llama_code_7b

SGLang

How to use syzymon/long_llama_code_7b 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 "syzymon/long_llama_code_7b" \
    --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": "syzymon/long_llama_code_7b",
		"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 "syzymon/long_llama_code_7b" \
        --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": "syzymon/long_llama_code_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use syzymon/long_llama_code_7b with Docker Model Runner:
```
docker model run hf.co/syzymon/long_llama_code_7b
```

syzymon commited on Sep 21, 2023

Commit

d7488ec

1 Parent(s): c879d23

Update README.md

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@@ -60,9 +60,9 @@ model-index:
 <div align="center">
- [TLDR](#TLDR) | [Overview](#Overview) | [Usage](#Usage) | [LongLLaMA performance](#LongLLaMA-performance) | [Authors](#Authors) | [Citation](#Citation) | [License](#License) | [Acknowledgments](#Acknowledgments)
- [FoT continued pretraining](fot_continued_pretraining) | [Instruction tuning](instruction_fine_tuning)
 </div>

 <div align="center">
+ [TLDR](#TLDR) | [Overview](#Overview) | [Results](#Results) | [Usage](#Usage) | [Authors](#Authors) | [Citation](#Citation) | [License](#License) | [Acknowledgments](#Acknowledgments)
+ [FoT continued pretraining](https://github.com/CStanKonrad/long_llama/tree/main/fot_continued_pretraining) | [Instruction tuning](https://github.com/CStanKonrad/long_llama/tree/main/instruction_fine_tuning)
 </div>