Instructions to use LeroyDyer/LCARS_TOP_SCORE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/LCARS_TOP_SCORE with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/LCARS_TOP_SCORE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/LCARS_TOP_SCORE")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/LCARS_TOP_SCORE")
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

vLLM

How to use LeroyDyer/LCARS_TOP_SCORE with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LeroyDyer/LCARS_TOP_SCORE

SGLang

How to use LeroyDyer/LCARS_TOP_SCORE 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 "LeroyDyer/LCARS_TOP_SCORE" \
    --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": "LeroyDyer/LCARS_TOP_SCORE",
		"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 "LeroyDyer/LCARS_TOP_SCORE" \
        --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": "LeroyDyer/LCARS_TOP_SCORE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LeroyDyer/LCARS_TOP_SCORE with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/LCARS_TOP_SCORE
```

Adding Evaluation Results

by LeroyDyer - opened Jul 21, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+117

-8

Files changed (1) hide show

README.md +117 -8

README.md CHANGED Viewed

@@ -1,9 +1,7 @@
 ---
-base_model:
-- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
-- yam-peleg/Experiment26-7B
-- liminerity/M7-7b
-- LeroyDyer/Mixtral_AI_Cyber_3.1_SFT
 library_name: transformers
 tags:
 - chemistry
@@ -15,9 +13,106 @@ tags:
 - finance
 - legal
 - medical
-license: openrail
-language:
-- en
 ---
 Used as the Boss of Other Agents!
@@ -64,3 +159,17 @@ VERY GOOD MODEL !!!!! (HIGH SCORES) - 78.9 Average
   url          = {https://doi.org/10.5281/zenodo.5371628},
 }

 ---
+language:
+- en
+license: openrail
 library_name: transformers
 tags:
 - chemistry
 - finance
 - legal
 - medical
+base_model:
+- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
+- yam-peleg/Experiment26-7B
+- liminerity/M7-7b
+- LeroyDyer/Mixtral_AI_Cyber_3.1_SFT
+model-index:
+- name: LCARS_TOP_SCORE
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 43.71
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 31.7
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 6.72
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 4.81
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 12.43
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 22.57
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=LeroyDyer/LCARS_TOP_SCORE
+      name: Open LLM Leaderboard
 ---
 Used as the Boss of Other Agents!
   url          = {https://doi.org/10.5281/zenodo.5371628},
 }
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/LeroyDyer__LCARS_TOP_SCORE-details)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |20.32|
+|IFEval (0-Shot)    |43.71|
+|BBH (3-Shot)       |31.70|
+|MATH Lvl 5 (4-Shot)| 6.72|
+|GPQA (0-shot)      | 4.81|
+|MuSR (0-shot)      |12.43|
+|MMLU-PRO (5-shot)  |22.57|