Instructions to use bhenrym14/platypus-yi-34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bhenrym14/platypus-yi-34b with Transformers:

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

pipe = pipeline("text-generation", model="bhenrym14/platypus-yi-34b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bhenrym14/platypus-yi-34b")
model = AutoModelForCausalLM.from_pretrained("bhenrym14/platypus-yi-34b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bhenrym14/platypus-yi-34b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bhenrym14/platypus-yi-34b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhenrym14/platypus-yi-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bhenrym14/platypus-yi-34b

SGLang

How to use bhenrym14/platypus-yi-34b 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 "bhenrym14/platypus-yi-34b" \
    --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": "bhenrym14/platypus-yi-34b",
		"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 "bhenrym14/platypus-yi-34b" \
        --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": "bhenrym14/platypus-yi-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bhenrym14/platypus-yi-34b with Docker Model Runner:
```
docker model run hf.co/bhenrym14/platypus-yi-34b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Instruction tune of Yi-34b with Open-Platypus (fp16)

Overview

This is chargoddard/Yi-34B-Llama, with instruction tuning performed with the garage-bAInd/Open-Platypus dataset. That base model is 01-ai/Yi-34B, but using llama2 model definitions and tokenizer to remove any remote code requirements.

This is a (merged) QLoRA fine-tune (rank 64).

The finetune was performed with 1x RTX 6000 Ada (~18 hours to this checkpoint). It is possible this is rather undertrained, as this checkpoint is at 1 epoch. I began to see some performance degradation after that; more hyperparameter tuning is probably warranted.

How to Use

Use as you would any llama-2 model.

Prompting:

Model was trained with legacy airoboros <2.0 system prompt. See bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16 model card for details.

Downloads last month: 205

Safetensors

Model size

34B params

Tensor type

F16

Model tree for bhenrym14/platypus-yi-34b

Quantizations

6 models

bhenrym14
/

platypus-yi-34b

Instruction tune of Yi-34b with Open-Platypus (fp16)

Overview

How to Use

Prompting:

Model tree for bhenrym14/platypus-yi-34b

Dataset used to train bhenrym14/platypus-yi-34b

Spaces using bhenrym14/platypus-yi-34b 2