Instructions to use teknium/airoboros-mistral2.2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/airoboros-mistral2.2-7b with Transformers:

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

pipe = pipeline("text-generation", model="teknium/airoboros-mistral2.2-7b")

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

tokenizer = AutoTokenizer.from_pretrained("teknium/airoboros-mistral2.2-7b")
model = AutoModelForCausalLM.from_pretrained("teknium/airoboros-mistral2.2-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use teknium/airoboros-mistral2.2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/airoboros-mistral2.2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/airoboros-mistral2.2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teknium/airoboros-mistral2.2-7b

SGLang

How to use teknium/airoboros-mistral2.2-7b 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 "teknium/airoboros-mistral2.2-7b" \
    --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": "teknium/airoboros-mistral2.2-7b",
		"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 "teknium/airoboros-mistral2.2-7b" \
        --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": "teknium/airoboros-mistral2.2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teknium/airoboros-mistral2.2-7b with Docker Model Runner:
```
docker model run hf.co/teknium/airoboros-mistral2.2-7b
```

teknium commited on Oct 3, 2023

Commit

d2fa1fe

1 Parent(s): 731cc2e

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -1

README.md CHANGED Viewed

@@ -32,6 +32,9 @@ hf-causal-experimental (pretrained=/home/teknium/dakota/lm-evaluation-harness/ai
 |-------------|------:|------|-----:|---|-----:|
 |truthfulqa_mc|      1|mc1   |0.3562|±  |0.0168|
 |             |       |mc2   |0.5217|±  |0.0156|
-```
 More info to come

 |-------------|------:|------|-----:|---|-----:|
 |truthfulqa_mc|      1|mc1   |0.3562|±  |0.0168|
 |             |       |mc2   |0.5217|±  |0.0156|
+```
+Wandb training charts: https://wandb.ai/teknium1/airoboros-mistral-7b/runs/airoboros-mistral-1?workspace=user-teknium1
 More info to come