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

Libraries

How to use KnutJaegersberg/MistralInstructLongish with Transformers:

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

pipe = pipeline("text-generation", model="KnutJaegersberg/MistralInstructLongish", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/MistralInstructLongish", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("KnutJaegersberg/MistralInstructLongish", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KnutJaegersberg/MistralInstructLongish with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KnutJaegersberg/MistralInstructLongish

SGLang

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

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

Adding Evaluation Results

by leaderboard-pr-bot - opened Nov 20, 2023

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+14

-1

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -13,4 +13,17 @@ blablabla
 ### Input:
 (optional)
 ### Response:
-```

 ### Input:
 (optional)
 ### Response:
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 48.99   |
+| ARC (25-shot)         | 60.75          |
+| HellaSwag (10-shot)   | 81.86    |
+| MMLU (5-shot)         | 60.49         |
+| TruthfulQA (0-shot)   | 40.55   |
+| Winogrande (5-shot)   | 76.56   |
+| GSM8K (5-shot)        | 1.52        |
+| DROP (3-shot)         | 21.22         |