Instructions to use PursuitOfDataScience/argonne-3.5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PursuitOfDataScience/argonne-3.5-base with Transformers:

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

pipe = pipeline("text-generation", model="PursuitOfDataScience/argonne-3.5-base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PursuitOfDataScience/argonne-3.5-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PursuitOfDataScience/argonne-3.5-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PursuitOfDataScience/argonne-3.5-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PursuitOfDataScience/argonne-3.5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PursuitOfDataScience/argonne-3.5-base

SGLang

How to use PursuitOfDataScience/argonne-3.5-base 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 "PursuitOfDataScience/argonne-3.5-base" \
    --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": "PursuitOfDataScience/argonne-3.5-base",
		"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 "PursuitOfDataScience/argonne-3.5-base" \
        --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": "PursuitOfDataScience/argonne-3.5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PursuitOfDataScience/argonne-3.5-base with Docker Model Runner:
```
docker model run hf.co/PursuitOfDataScience/argonne-3.5-base
```

argonne-3.5-base

File size: 1,179 Bytes

63cbe49

{
  "_keep_in_fp32_modules": [],
  "architectures": [
    "ArgonneModel"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "block_size": 13568,
  "bos_token_id": null,
  "dtype": "bfloat16",
  "eos_token_id": null,
  "hidden_dropout": 0.0,
  "hidden_size": 3072,
  "interleaved_local_attention": true,
  "intermediate_size": 8192,
  "local_attention_window": 256,
  "logit_softcap": 15.0,
  "loss_chunk_size": 0,
  "max_position_embeddings": 13568,
  "mlp_bias": false,
  "model_type": "argonne2",
  "mtp_horizon": 1,
  "mtp_loss_weight": 0.0,
  "n_embd": 3072,
  "n_head": 12,
  "n_layer": 24,
  "num_attention_heads": 12,
  "num_hidden_layers": 24,
  "num_key_value_heads": 4,
  "pad_token_id": null,
  "qk_norm": true,
  "rms_norm_eps": 1e-06,
  "rope_theta": 1000000.0,
  "sandwich_norm": true,
  "sliding_window": null,
  "tie_word_embeddings": true,
  "transformers_version": "5.6.2",
  "use_flash_attention": true,
  "use_gradient_checkpointing": false,
  "v_norm": true,
  "vocab_size": 151669,
  "z_loss_weight": 0.0,
  "torch_dtype": "bfloat16",
  "auto_map": {
    "AutoConfig": "model.ArgonneConfig",
    "AutoModelForCausalLM": "model.ArgonneModel"
  }
}