Instructions to use aloobun/bun_mistral_7b_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aloobun/bun_mistral_7b_v2 with Transformers:

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

pipe = pipeline("text-generation", model="aloobun/bun_mistral_7b_v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aloobun/bun_mistral_7b_v2")
model = AutoModelForCausalLM.from_pretrained("aloobun/bun_mistral_7b_v2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aloobun/bun_mistral_7b_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aloobun/bun_mistral_7b_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aloobun/bun_mistral_7b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aloobun/bun_mistral_7b_v2

SGLang

How to use aloobun/bun_mistral_7b_v2 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 "aloobun/bun_mistral_7b_v2" \
    --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": "aloobun/bun_mistral_7b_v2",
		"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 "aloobun/bun_mistral_7b_v2" \
        --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": "aloobun/bun_mistral_7b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aloobun/bun_mistral_7b_v2 with Docker Model Runner:
```
docker model run hf.co/aloobun/bun_mistral_7b_v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

finetuned of mistralai/Mistral-7B-v0.1 for CoT reasoning

Fine-tuning language models is like tuning the strings of an AI banjo in the cosmic saloon of the digital frontier. We're not just slinging code; it's a harmonious quest to shape the minds of silicon wanderers, crafting binary ballads and electronic echoes. Picture it as cybernetic bardic magic, where we, the tech sorcerers, weave algorithms with strands of imagination. But, in this cosmic hoedown, there's a twist – as we twang the strings of artificial intelligence, we're also seeding the algorithms with a bit of human stardust, adding quirks and quirksome biases. So, as we two-step into this frontier of creation, are we summoning AI troubadours of the future or just conjuring interstellar jesters, spinning tales of silicon whimsy and digital campfire banter?

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