Instructions to use BlouseJury/Mistral-7B-Discord-0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlouseJury/Mistral-7B-Discord-0.2 with Transformers:

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

pipe = pipeline("text-generation", model="BlouseJury/Mistral-7B-Discord-0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BlouseJury/Mistral-7B-Discord-0.2")
model = AutoModelForCausalLM.from_pretrained("BlouseJury/Mistral-7B-Discord-0.2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BlouseJury/Mistral-7B-Discord-0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BlouseJury/Mistral-7B-Discord-0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BlouseJury/Mistral-7B-Discord-0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BlouseJury/Mistral-7B-Discord-0.2

SGLang

How to use BlouseJury/Mistral-7B-Discord-0.2 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 "BlouseJury/Mistral-7B-Discord-0.2" \
    --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": "BlouseJury/Mistral-7B-Discord-0.2",
		"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 "BlouseJury/Mistral-7B-Discord-0.2" \
        --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": "BlouseJury/Mistral-7B-Discord-0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BlouseJury/Mistral-7B-Discord-0.2 with Docker Model Runner:
```
docker model run hf.co/BlouseJury/Mistral-7B-Discord-0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-7B-Discord-0.1

This model is a finetune of Mistral-7B-0.1 on ~40 Million tokens worth of mostly not formatted, anonymized discord messages for 4 Epochs.

This is a base model.

Model Details

Finetuned from model : mistralai/Mistral-7B-v0.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	59.55
AI2 Reasoning Challenge (25-Shot)	60.58
HellaSwag (10-Shot)	82.49
MMLU (5-Shot)	62.82
TruthfulQA (0-shot)	42.73
Winogrande (5-shot)	77.74
GSM8k (5-shot)	30.93

Downloads last month: 68

Safetensors

Model size

7B params

Tensor type

F16

Model tree for BlouseJury/Mistral-7B-Discord-0.2

Quantizations

3 models

Spaces using BlouseJury/Mistral-7B-Discord-0.2 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

60.580
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

82.490
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

62.820
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

42.730
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.740
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

30.930