Instructions to use argo11/0399-tv-full-thinking-fp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argo11/0399-tv-full-thinking-fp with Transformers:

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

pipe = pipeline("text-generation", model="argo11/0399-tv-full-thinking-fp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("argo11/0399-tv-full-thinking-fp")
model = AutoModelForCausalLM.from_pretrained("argo11/0399-tv-full-thinking-fp", device_map="auto")
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 Settings

vLLM

How to use argo11/0399-tv-full-thinking-fp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "argo11/0399-tv-full-thinking-fp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argo11/0399-tv-full-thinking-fp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/argo11/0399-tv-full-thinking-fp

SGLang

How to use argo11/0399-tv-full-thinking-fp 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 "argo11/0399-tv-full-thinking-fp" \
    --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": "argo11/0399-tv-full-thinking-fp",
		"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 "argo11/0399-tv-full-thinking-fp" \
        --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": "argo11/0399-tv-full-thinking-fp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use argo11/0399-tv-full-thinking-fp with Docker Model Runner:
```
docker model run hf.co/argo11/0399-tv-full-thinking-fp
```

0399-tv-full-thinking-fp / validate_summary.json

argo11

Training in progress, step 500

c8240c1 verified about 1 month ago

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	{
	"provenance": {
	"experiment_id": "0399",
	"experiment_slug": "tv_sft",
	"experiment_dir": "/groups/gcg51557/experiments/0399_tv_sft",
	"created_at": "2026-06-30T01:42:39.864323+00:00",
	"command_line": [
	"scripts/run_sft_main_0399.py",
	"--config",
	"configs/0399/sft_full_thinking.yaml",
	"--validate_only",
	"--overwrite"
	],
	"cwd": "/home/ach18380vf/llmc_math_victory",
	"git_commit": null,
	"python": {
	"executable": "/groups/gcg51557/experiments/0399_tv_sft/.venv/bin/python",
	"version": "3.12.9 (tags/v3.12.9:fdb81425a9a, Apr 5 2025, 14:26:51) [GCC 11.5.0 20240719 (Red Hat 11.5.0-5)]",
	"platform": "Linux-5.14.0-427.13.1.el9_4.x86_64-x86_64-with-glibc2.34"
	},
	"env": {
	"EXP_DIR": "/groups/gcg51557/experiments/0399_tv_sft",
	"EXPERIMENT_ID": "0399",
	"EXPERIMENT_SLUG": "tv_sft",
	"HF_HOME": "/groups/gcg51557/experiments/0399_tv_sft/cache/huggingface",
	"HF_DATASETS_CACHE": "/groups/gcg51557/experiments/0399_tv_sft/cache/huggingface/datasets",
	"TRANSFORMERS_CACHE": "/groups/gcg51557/experiments/0399_tv_sft/cache/huggingface/hub",
	"HUGGINGFACE_HUB_CACHE": "/groups/gcg51557/experiments/0399_tv_sft/cache/huggingface/hub",
	"WANDB_DIR": "/groups/gcg51557/experiments/0399_tv_sft/wandb",
	"WANDB_PROJECT": "llmjp4-8b-teamvictory-sft-difficulty-20260629",
	"WANDB_ENTITY": "argo-lab"
	}
	},
	"config_path": "/home/ach18380vf/llmc_math_victory/configs/0399/sft_full_thinking.yaml",
	"output_dir": "/groups/gcg51557/experiments/0399_tv_sft/checkpoints/tv_full_thinking_fp",
	"dataset_stats": {
	"input_rows": 8,
	"train_rows": 8,
	"skipped_all_masked": 0
	},
	"model_stats": {
	"train_mode": "full_param"
	},
	"validate_only": true
	}