Instructions to use ogmatrixllm/arcadex-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ogmatrixllm/arcadex-llm with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ogmatrixllm/arcadex-llm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ogmatrixllm/arcadex-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ogmatrixllm/arcadex-llm

SGLang

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

Docker Model Runner
How to use ogmatrixllm/arcadex-llm with Docker Model Runner:
```
docker model run hf.co/ogmatrixllm/arcadex-llm
```

ogmatrixllm commited on Jan 11, 2025

Commit

80fa9fb

verified ·

1 Parent(s): 7a14701

Update README.md

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README.md +18 -17

README.md CHANGED Viewed

@@ -25,27 +25,28 @@ base_model:
 pipeline_tag: text-generation
 ---
-    # Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration
-    This is a LoRA-fused model based on **Qwen/Qwen2.5-7B-Instruct**.
-    ## Model Description
-    - **Model Name**: Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration
-    - **Language**: en
-    - **License**: apache-2.0
-    - **Dataset**: ogmatrixllm/pokemon-lore-instructions
-    - **Tags**: LoRA, 4-bit, BF16, FlashAttn2, Pokémon, EMA, fast-training, text-generation, chat, transformers
-    ## Usage
-    ```python
-    from transformers import AutoTokenizer, AutoModelForCausalLM
-    tokenizer = AutoTokenizer.from_pretrained("ogmatrixllm/arcadex-llm")
-    model = AutoModelForCausalLM.from_pretrained("ogmatrixllm/arcadex-llm")
-    prompt = "Hello, world!"
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs)
-    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

 pipeline_tag: text-generation
 ---
+# Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration
+This is a LoRA-fused model based on **Qwen/Qwen2.5-7B-Instruct**.
+## Model Description
+- **Model Name**: Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration
+- **Language**: en
+- **License**: apache-2.0
+- **Dataset**: ogmatrixllm/pokemon-lore-instructions
+- **Tags**: LoRA, 4-bit, BF16, FlashAttn2, Pokémon, EMA, fast-training, text-generation, chat, transformers
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("ogmatrixllm/arcadex-llm")
+model = AutoModelForCausalLM.from_pretrained("ogmatrixllm/arcadex-llm")
+prompt = "Hello, world!"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```