Instructions to use debisoft/mpt-7b-8k-instruct-peft-compatible with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use debisoft/mpt-7b-8k-instruct-peft-compatible with Transformers:

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

pipe = pipeline("text-generation", model="debisoft/mpt-7b-8k-instruct-peft-compatible", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("debisoft/mpt-7b-8k-instruct-peft-compatible", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("debisoft/mpt-7b-8k-instruct-peft-compatible", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use debisoft/mpt-7b-8k-instruct-peft-compatible with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "debisoft/mpt-7b-8k-instruct-peft-compatible"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "debisoft/mpt-7b-8k-instruct-peft-compatible",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/debisoft/mpt-7b-8k-instruct-peft-compatible

SGLang

How to use debisoft/mpt-7b-8k-instruct-peft-compatible 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 "debisoft/mpt-7b-8k-instruct-peft-compatible" \
    --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": "debisoft/mpt-7b-8k-instruct-peft-compatible",
		"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 "debisoft/mpt-7b-8k-instruct-peft-compatible" \
        --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": "debisoft/mpt-7b-8k-instruct-peft-compatible",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use debisoft/mpt-7b-8k-instruct-peft-compatible with Docker Model Runner:
```
docker model run hf.co/debisoft/mpt-7b-8k-instruct-peft-compatible
```

Librarian Bot: Update Hugging Face dataset ID

by librarian-bot - opened Apr 11, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -1,17 +1,17 @@
 ---
 license: cc-by-sa-3.0
 datasets:
-- competition_math
 - knkarthick/dialogsum
 - mosaicml/dolly_hhrlhf
 - duorc
 - emozilla/quality
 - scrolls/summ_screen_fd
 - spider
-tags:
-- Composer
-- MosaicML
-- llm-foundry
 inference: false
 ---

 ---
 license: cc-by-sa-3.0
+tags:
+- Composer
+- MosaicML
+- llm-foundry
 datasets:
+- hendrycks/competition_math
 - knkarthick/dialogsum
 - mosaicml/dolly_hhrlhf
 - duorc
 - emozilla/quality
 - scrolls/summ_screen_fd
 - spider
 inference: false
 ---