Instructions to use spmurrayzzz/Mistral-Syndicate-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spmurrayzzz/Mistral-Syndicate-7B with Transformers:

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

pipe = pipeline("text-generation", model="spmurrayzzz/Mistral-Syndicate-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("spmurrayzzz/Mistral-Syndicate-7B")
model = AutoModelForCausalLM.from_pretrained("spmurrayzzz/Mistral-Syndicate-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use spmurrayzzz/Mistral-Syndicate-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/spmurrayzzz/Mistral-Syndicate-7B

SGLang

How to use spmurrayzzz/Mistral-Syndicate-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 "spmurrayzzz/Mistral-Syndicate-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": "spmurrayzzz/Mistral-Syndicate-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 "spmurrayzzz/Mistral-Syndicate-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": "spmurrayzzz/Mistral-Syndicate-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use spmurrayzzz/Mistral-Syndicate-7B with Docker Model Runner:
```
docker model run hf.co/spmurrayzzz/Mistral-Syndicate-7B
```

spmurrayzzz

leaderboard-pr-bot commited on Jul 26, 2024

Commit

7fa40e3

verified ·

1 Parent(s): c74379d

Adding Evaluation Results (#3)

Browse files

- Adding Evaluation Results (a59405638b4f50675bd88c9d14979862c107630d)

Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>

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README.md +14 -0

README.md CHANGED Viewed

@@ -76,3 +76,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 |Winogrande (5-shot)              |78.61|
 |GSM8k (5-shot)                   |44.50|

 |Winogrande (5-shot)              |78.61|
 |GSM8k (5-shot)                   |44.50|
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_spmurrayzzz__Mistral-Syndicate-7B)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |13.85|
+|IFEval (0-Shot)    |24.96|
+|BBH (3-Shot)       |20.51|
+|MATH Lvl 5 (4-Shot)| 2.42|
+|GPQA (0-shot)      | 3.47|
+|MuSR (0-shot)      |13.62|
+|MMLU-PRO (5-shot)  |18.13|