Instructions to use exnivo/Echo88-150M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use exnivo/Echo88-150M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="exnivo/Echo88-150M-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("exnivo/Echo88-150M-Instruct")
model = AutoModelForCausalLM.from_pretrained("exnivo/Echo88-150M-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use exnivo/Echo88-150M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "exnivo/Echo88-150M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "exnivo/Echo88-150M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/exnivo/Echo88-150M-Instruct

SGLang

How to use exnivo/Echo88-150M-Instruct 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 "exnivo/Echo88-150M-Instruct" \
    --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": "exnivo/Echo88-150M-Instruct",
		"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 "exnivo/Echo88-150M-Instruct" \
        --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": "exnivo/Echo88-150M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use exnivo/Echo88-150M-Instruct with Docker Model Runner:
```
docker model run hf.co/exnivo/Echo88-150M-Instruct
```

Echo88-150M-Instruct / sft_training_info.json

Guus

Upload folder using huggingface_hub

e9dadd9 verified 3 months ago

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	{
	"base_model": "/content/echo88_final/Echo88-150M-Instruct",
	"instruct_dataset": "/content/echo88/echo88_fact_repair_20k.jsonl",
	"max_length": 1024,
	"micro_batch": 64,
	"grad_accum": 2,
	"epochs": 4,
	"learning_rate": 1e-05,
	"train_examples": 19500,
	"eval_examples": 500,
	"system_prompt": "You are Echo88, a helpful computer assistant whose records go up to the end of 1988. Answer clearly. Do not pretend to know events, products, or culture after 1988."
	}