Instructions to use macadeliccc/piccolo-2x7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/piccolo-2x7b with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/piccolo-2x7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/piccolo-2x7b")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/piccolo-2x7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macadeliccc/piccolo-2x7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/piccolo-2x7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/piccolo-2x7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/macadeliccc/piccolo-2x7b

SGLang

How to use macadeliccc/piccolo-2x7b 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 "macadeliccc/piccolo-2x7b" \
    --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": "macadeliccc/piccolo-2x7b",
		"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 "macadeliccc/piccolo-2x7b" \
        --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": "macadeliccc/piccolo-2x7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use macadeliccc/piccolo-2x7b with Docker Model Runner:
```
docker model run hf.co/macadeliccc/piccolo-2x7b
```

macadeliccc commited on Jan 17, 2024

Commit

4e5ca25

verified ·

1 Parent(s): dd5efb2

Update README.md

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README.md CHANGED Viewed

@@ -123,4 +123,34 @@ The model is capable of quality code, math, and logical reasoning. Try whatever
 | truthfulqa_mc | 1 | mc1 | 47.37 | ± 1.75 |
 |  |  | mc2 | 63.96 | ± 1.57 |
-**Average:

 | truthfulqa_mc | 1 | mc1 | 47.37 | ± 1.75 |
 |  |  | mc2 | 63.96 | ± 1.57 |
+**Average: 63.96%**
+## Bigbench
+### Tasks and Results
+| Task | Version | Metric | Value | Stderr |
+|------|---------|--------|-------|--------|
+| bigbench_causal_judgement | 0 | multiple_choice_grade | 55.26 | ± 3.62 |
+| bigbench_date_understanding | 0 | multiple_choice_grade | 63.14 | ± 2.51 |
+| bigbench_disambiguation_qa | 0 | multiple_choice_grade | 42.64 | ± 3.08 |
+| bigbench_geometric_shapes | 0 | multiple_choice_grade | 22.84 | ± 2.22 |
+| | | exact_str_match | 3.34 | ± 0.95 |
+| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade | 36.60 | ± 2.16 |
+| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade | 25.57 | ± 1.65 |
+| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade | 56.00 | ± 2.87 |
+| bigbench_movie_recommendation | 0 | multiple_choice_grade | 42.40 | ± 2.21 |
+| bigbench_navigate | 0 | multiple_choice_grade | 54.70 | ± 1.57 |
+| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade | 62.90 | ± 1.08 |
+| bigbench_ruin_names | 0 | multiple_choice_grade | 53.35 | ± 2.36 |
+| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade | 24.35 | ± 1.36 |
+| bigbench_snarks | 0 | multiple_choice_grade | 62.43 | ± 3.61 |
+| bigbench_sports_understanding | 0 | multiple_choice_grade | 70.28 | ± 1.46 |
+| bigbench_temporal_sequences | 0 | multiple_choice_grade | 41.30 | ± 1.56 |
+| bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade | 22.32 | ± 1.18 |
+| bigbench_tracking_shuffled_objects_seven_objects | 0 | multiple_choice_grade | 17.77 | ± 0.91 |
+| bigbench_tracking_shuffled_objects_three_objects | 0 | multiple_choice_grade | 56.00 | ± 2.87 |
+### Overall Average Score
+**Average score: 56.96%**