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

Libraries

How to use PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0

SGLang

How to use PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 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 "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0" \
    --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": "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0",
		"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 "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0" \
        --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": "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0
```

kyujinpy commited on Dec 30, 2023

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de23670

1 Parent(s): a4ed7b3

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@@ -21,7 +21,28 @@ Instruction-tuning with [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggin
 datasets: [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).
 **Hyperparameters**
-(I will update all!)
 # **Model Benchmark**

 datasets: [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).
 **Hyperparameters**
+```python
+python finetune.py \
+    --base_model PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 \
+    --data-path  kyujinpy/KOR-OpenOrca-Platypus-v3 \
+    --output_dir ./SOLAR-tail-10.7B-instruct \
+    --batch_size 64 \
+    --micro_batch_size 1 \
+    --num_epochs 1 \
+    --learning_rate 3e-5 \
+    --cutoff_len 4096 \
+    --val_set_size 0 \
+    --lora_r 16 \
+    --lora_alpha 16 \
+    --lora_dropout 0.05 \
+    --lora_target_modules '[q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \
+    --train_on_inputs False \
+    --add_eos_token False \
+    --group_by_length False \
+    --prompt_template_name user_prompt \
+    --lr_scheduler 'cosine' \
+```
+> Platypus repo.
 # **Model Benchmark**