Instructions to use CorticalStack/mistral-7b-tak-stack-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CorticalStack/mistral-7b-tak-stack-dpo with Transformers:

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

pipe = pipeline("text-generation", model="CorticalStack/mistral-7b-tak-stack-dpo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CorticalStack/mistral-7b-tak-stack-dpo")
model = AutoModelForCausalLM.from_pretrained("CorticalStack/mistral-7b-tak-stack-dpo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CorticalStack/mistral-7b-tak-stack-dpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CorticalStack/mistral-7b-tak-stack-dpo

SGLang

How to use CorticalStack/mistral-7b-tak-stack-dpo 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 "CorticalStack/mistral-7b-tak-stack-dpo" \
    --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": "CorticalStack/mistral-7b-tak-stack-dpo",
		"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 "CorticalStack/mistral-7b-tak-stack-dpo" \
        --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": "CorticalStack/mistral-7b-tak-stack-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CorticalStack/mistral-7b-tak-stack-dpo with Docker Model Runner:
```
docker model run hf.co/CorticalStack/mistral-7b-tak-stack-dpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

mistral-7b-tak-stack-dpo

mistral-7b-tak-stack-dpo is a DPO fine-tuned version of mistralai/Mistral-7B-v0.1 using the CorticalStack/tak-stack-dpo dataset.

LoRA

r: 32
LoRA alpha: 32
LoRA dropout: 0.05

Training arguments

Batch size: 4
Gradient accumulation steps: 4
Optimizer: paged_adamw_32bit
Max steps: 100
Learning rate: 5e-05
Learning rate scheduler type: cosine
Beta: 0.1
Max prompt length: 1024
Max length: 1536

Downloads last month: 49

Safetensors

Model size

7B params

Tensor type

F16

Model tree for CorticalStack/mistral-7b-tak-stack-dpo

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(947)

this model

Quantizations

2 models

CorticalStack
/

mistral-7b-tak-stack-dpo

mistral-7b-tak-stack-dpo

LoRA

Training arguments

Model tree for CorticalStack/mistral-7b-tak-stack-dpo

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