Instructions to use BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k with Transformers:

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

pipe = pipeline("text-generation", model="BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k

SGLang

How to use BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k 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 "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k" \
    --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": "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k",
		"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 "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k" \
        --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": "BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k
```

Mistral-7B-v0.3-stepbasin-books-20480

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on this dataset for the purpose of testing out super-long text generation.

fine-tuned at context length 20480, should consistently generate 8k+ tokens (example)

It achieves the following results on the evaluation set:

Loss: 2.0784
Accuracy: 0.5396
Num Input Tokens Seen: 16384000

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for BEE-spoke-data/Mistral-7B-v0.3-stepbasin-books-20k

Base model

mistralai/Mistral-7B-v0.3

Finetuned

(461)

this model