Instructions to use RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits")

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

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits

SGLang

How to use RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits 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 "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits" \
    --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": "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits",
		"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 "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits" \
        --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": "RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/Locutusque_-_TinyMistral-248M-Instruct-8bits
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

TinyMistral-248M-Instruct - bnb 8bits

Model creator: https://huggingface.co/Locutusque/
Original model: https://huggingface.co/Locutusque/TinyMistral-248M-Instruct/

Original model description:

pipeline_tag: text-generation base_model: Locutusque/TinyMistral-248M license: apache-2.0 datasets: - Locutusque/InstructMixCleaned - berkeley-nest/Nectar language: - en widget: - text: >- <|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else or switch/case statements to handle different conditions related to the Consent. Provide detailed comments explaining your control flow and the reasoning behind each decision. <|ASSISTANT|> - text: >- <|USER|> Write me a story about a magical place. <|ASSISTANT|> - text: >- <|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> - text: >- <|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> - text: >- <|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> - text: >- <|USER|> How to manage a lazy employee: Address the employee verbally. Don't allow an employee's laziness or lack of enthusiasm to become a recurring issue. Tell the employee you're hoping to speak with them about workplace expectations and performance, and schedule a time to sit down together. Question: To manage a lazy employee, it is suggested to talk to the employee. True, False, or Neither? <|ASSISTANT|> inference: parameters: temperature: 0.5 do_sample: True top_p: 0.5 top_k: 30 max_new_tokens: 250 repetition_penalty: 1.15

Base model Locutusque/TinyMistral-248M fully fine-tuned on Locutusque/InstructMix. During validation, this model achieved an average perplexity of 3.23 on Locutusque/InstructMix dataset. It has so far been trained on approximately 608,000 examples. More epochs are planned for this model.

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

F32

F16