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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-Syndicate-7B

Model Description:

Mistral Syndicate is in no way a state-of-the-art model, rather it is a fine-tuning experiment to explore the training dynamics specific to large language models. The dataset used in finetuning was generated via a "syndicate" of other open language models both of similar parameter size and larger. Each model would generate a response for a given instruction, and the group would vote on which model's response was best.

The instruction inputs used for the output label synthesis were a curated subset of VMWare/open-instruct with additional instructions synthesized from scratch.

Prompt template

With context

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:

### Input:

### Response:

Without context

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:

### Response:

Evaluation Results

12.30.23

Benchmark	Result
ARC	60.84
HellaSwag	82.91
MMLU	60.83
TruthfulQA	43.71
Winogrande	78.61
GSM8K	44.50

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.90
AI2 Reasoning Challenge (25-Shot)	60.84
HellaSwag (10-Shot)	82.91
MMLU (5-Shot)	60.83
TruthfulQA (0-shot)	43.71
Winogrande (5-shot)	78.61
GSM8k (5-shot)	44.50

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for spmurrayzzz/Mistral-Syndicate-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

Quantizations

2 models