Instructions to use macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser

SGLang

How to use macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser 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/SOLAR-10.7B-Instruct-v1.0-laser" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/SOLAR-10.7B-Instruct-v1.0-laser" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser with Docker Model Runner:
```
docker model run hf.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser
```

macadeliccc commited on Feb 13, 2024

Commit

82700c8

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1 Parent(s): 41e1370

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@@ -9,3 +9,83 @@ This version of Solar-10.7B was lasered and perplexity was calculated against gs
 + New baseline perplexity: 12.554274559020996
 The laser process decreased perplexity by 2.41%

 + New baseline perplexity: 12.554274559020996
 The laser process decreased perplexity by 2.41%
+|                                                Model                                                |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
+|-----------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
+|[SOLAR-10.7B-Instruct-v1.0-laser](https://huggingface.co/macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser)|   46.9|  74.99|     70.64|   43.74|  59.07|
+### AGIEval
+|             Task             |Version| Metric |Value|   |Stderr|
+|------------------------------|------:|--------|----:|---|-----:|
+|agieval_aqua_rat              |      0|acc     |29.53|±  |  2.87|
+|                              |       |acc_norm|28.35|±  |  2.83|
+|agieval_logiqa_en             |      0|acc     |39.78|±  |  1.92|
+|                              |       |acc_norm|40.55|±  |  1.93|
+|agieval_lsat_ar               |      0|acc     |23.04|±  |  2.78|
+|                              |       |acc_norm|21.30|±  |  2.71|
+|agieval_lsat_lr               |      0|acc     |51.18|±  |  2.22|
+|                              |       |acc_norm|51.76|±  |  2.21|
+|agieval_lsat_rc               |      0|acc     |66.54|±  |  2.88|
+|                              |       |acc_norm|66.91|±  |  2.87|
+|agieval_sat_en                |      0|acc     |78.16|±  |  2.89|
+|                              |       |acc_norm|78.16|±  |  2.89|
+|agieval_sat_en_without_passage|      0|acc     |50.97|±  |  3.49|
+|                              |       |acc_norm|50.00|±  |  3.49|
+|agieval_sat_math              |      0|acc     |42.73|±  |  3.34|
+|                              |       |acc_norm|38.18|±  |  3.28|
+Average: 46.9%
+### GPT4All
+|    Task     |Version| Metric |Value|   |Stderr|
+|-------------|------:|--------|----:|---|-----:|
+|arc_challenge|      0|acc     |60.84|±  |  1.43|
+|             |       |acc_norm|63.99|±  |  1.40|
+|arc_easy     |      0|acc     |83.59|±  |  0.76|
+|             |       |acc_norm|81.44|±  |  0.80|
+|boolq        |      1|acc     |87.58|±  |  0.58|
+|hellaswag    |      0|acc     |68.11|±  |  0.47|
+|             |       |acc_norm|85.77|±  |  0.35|
+|openbookqa   |      0|acc     |35.40|±  |  2.14|
+|             |       |acc_norm|48.40|±  |  2.24|
+|piqa         |      0|acc     |80.58|±  |  0.92|
+|             |       |acc_norm|80.74|±  |  0.92|
+|winogrande   |      0|acc     |77.03|±  |  1.18|
+Average: 74.99%
+### TruthfulQA
+|    Task     |Version|Metric|Value|   |Stderr|
+|-------------|------:|------|----:|---|-----:|
+|truthfulqa_mc|      1|mc1   |55.45|±  |  1.74|
+|             |       |mc2   |70.64|±  |  1.49|
+Average: 70.64%
+### Bigbench
+|                      Task                      |Version|       Metric        |Value|   |Stderr|
+|------------------------------------------------|------:|---------------------|----:|---|-----:|
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|57.37|±  |  3.60|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|62.87|±  |  2.52|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|35.66|±  |  2.99|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|33.15|±  |  2.49|
+|                                                |       |exact_str_match      | 0.00|±  |  0.00|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|26.20|±  |  1.97|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|19.71|±  |  1.50|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|45.00|±  |  2.88|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|39.00|±  |  2.18|
+|bigbench_navigate                               |      0|multiple_choice_grade|51.20|±  |  1.58|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|53.90|±  |  1.11|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|40.18|±  |  2.32|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|39.98|±  |  1.55|
+|bigbench_snarks                                 |      0|multiple_choice_grade|63.54|±  |  3.59|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|68.36|±  |  1.48|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|65.20|±  |  1.51|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|22.48|±  |  1.18|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.46|±  |  0.93|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|45.00|±  |  2.88|
+Average: 43.74%
+Average score: 59.07%
+Elapsed time: 02:33:24