Instructions to use r-three/moose-arc_challenge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r-three/moose-arc_challenge with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "r-three/moose-arc_challenge")

Transformers

How to use r-three/moose-arc_challenge with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("r-three/moose-arc_challenge", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use r-three/moose-arc_challenge with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/r-three/moose-arc_challenge

SGLang

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

Docker Model Runner
How to use r-three/moose-arc_challenge with Docker Model Runner:
```
docker model run hf.co/r-three/moose-arc_challenge
```

moose-arc_challenge / adapter_config.json

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Add LawInformedAI,overruling,,None LoRA model

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	{
	"alpha_pattern": {},
	"auto_mapping": null,
	"base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
	"bias": "none",
	"corda_config": null,
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 16,
	"lora_bias": false,
	"lora_dropout": 0.1,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"qalora_group_size": 16,
	"r": 16,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"q_proj",
	"o_proj",
	"up_proj",
	"gate_proj",
	"down_proj",
	"k_proj",
	"v_proj"
	],
	"task_type": "CAUSAL_LM",
	"trainable_token_indices": null,
	"use_dora": false,
	"use_qalora": false,
	"use_rslora": false
	}