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

Libraries

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

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

pipe = pipeline("text-generation", model="argo11/0399-tv-full-base-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-base-fp")
model = AutoModelForCausalLM.from_pretrained("argo11/0399-tv-full-base-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-base-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-base-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-base-fp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

0399-tv-full-base-fp / README.md

argo11

Fix base_model metadata to llm-jp/llm-jp-4-8b-base

58f9209 verified about 2 months ago

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	---
	language:
	- ja
	- en
	license: other
	library_name: transformers
	pipeline_tag: text-generation
	base_model: llm-jp/llm-jp-4-8b-base
	tags:
	- llm-jp
	- math
	- sft
	- full-parameter-finetuning
	- team-victory
	- experiment-0399
	- wandb
	datasets:
	- HayatoHongoEveryonesAI/qa_verify_cot_new_6M_v6
	- argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b
	---

	# 0399 Team Victory Full-Parameter SFT Checkpoint

	This repository contains intermediate/full-parameter SFT checkpoints from LLM-jp experiment `0399`, using Team Victory valid-clean math reasoning data.

	## Model Variants

	Two model repositories were trained under the same data and training recipe, differing only in initialization:

	\| Repo \| Initialization \| W&B Run \|
	\|---\|---\|---\|
	\| [`argo11/0399-tv-full-thinking-fp`](https://huggingface.co/argo11/0399-tv-full-thinking-fp) \| [`llm-jp/llm-jp-4-8b-thinking`](https://huggingface.co/llm-jp/llm-jp-4-8b-thinking) \| [`0399_tv-full-thinking_fp`](https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629/runs/4d9n7h6v) \|
	\| [`argo11/0399-tv-full-base-fp`](https://huggingface.co/argo11/0399-tv-full-base-fp) \| [`llm-jp/llm-jp-4-8b-base`](https://huggingface.co/llm-jp/llm-jp-4-8b-base) \| [`0399_tv-full-base_fp`](https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629/runs/6spyiapq) \|

	Current uploaded checkpoint:

	- `checkpoint-500/`
	- Upload excludes large optimizer/FSDP duplicate states from Hub.
	- Local full training state, including optimizer state, is retained under the ABCI experiment directory.

	## Experiment Links

	- GitHub issue: [llm-jp/experiments#399](https://github.com/llm-jp/experiments/issues/399)
	- W&B project: [argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629](https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629)
	- Thinking W&B run: [0399_tv-full-thinking_fp](https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629/runs/4d9n7h6v)
	- Base W&B run: [0399_tv-full-base_fp](https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629/runs/6spyiapq)
	- Tokenized dataset: [argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b](https://huggingface.co/datasets/argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b)

	## Intended Use

	These checkpoints are intended for research on Japanese/English mathematical reasoning and reinforcement-learning initialization.

	Primary intended uses:

	- Compare `thinking` initialization vs `base` initialization after identical Team Victory SFT.
	- Use as candidate initial checkpoints for later RL / GRPO-style math reasoning experiments.
	- Evaluate whether Team Victory SFT improves AIME-style pass@1 while preserving broader ability.

	Not intended uses:

	- Production deployment without additional evaluation.
	- Safety-critical mathematical, financial, legal, medical, or educational grading decisions.
	- Claims of benchmark superiority before full evaluation is complete.

	## Training Data

	Source dataset:

	- [`HayatoHongoEveryonesAI/qa_verify_cot_new_6M_v6`](https://huggingface.co/datasets/HayatoHongoEveryonesAI/qa_verify_cot_new_6M_v6)

	Filtered dataset:

	- `TV_valid_clean`
	- Rows: `5,461,079`
	- Clean parquet SHA-256: `b1fbc4d5c05dbacbf9366055200a034551a46d70bfb2bff4c6432f2175a10d9b`
	- Filter rule:
	- `is_valid == 1`
	- contamination quarantine applied against target benchmark problems
	- Tokenized reusable dataset:
	- [`argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b`](https://huggingface.co/datasets/argo11/0399-tv-valid-clean-sft-tokenized-llmjp4-8b)
	- Rows: `5,461,079`
	- Shards: `110`
	- Max length: `4096`
	- Columns: `input_ids`, `attention_mask`, `labels`, `prompt_len`, `seq_len`

	The tokenized dataset was prepared on CPU and uploaded to Hugging Face to avoid repeated expensive tokenization on GPU nodes.

	## Training Procedure

	Training mode:

	- Full-parameter SFT
	- No LoRA / PEFT
	- Hugging Face `Trainer`
	- FSDP: `full_shard auto_wrap`
	- Assistant response tokens only are trained.
	- Prompt tokens are masked with `-100`.

	Core hyperparameters:

	\| Parameter \| Value \|
	\|---\|---:\|
	\| Epochs \| `1` \|
	\| Max length \| `4096` \|
	\| Per-device train batch size \| `1` \|
	\| Gradient accumulation steps \| `16` \|
	\| Learning rate \| `2.0e-5` \|
	\| Warmup ratio \| `0.03` \|
	\| LR scheduler \| `cosine` \|
	\| Precision \| `bf16` \|
	\| Optimizer \| `adamw_torch` \|
	\| Save steps \| `500` \|
	\| Save total limit \| `3` \|
	\| Seed \| `20260629` \|

	Infrastructure:

	- ABCI 3.0
	- Group: `gcg51557`
	- Reserved queue: `R9920261000`
	- Experiment directory: `/groups/gcg51557/experiments/0399_tv_sft`
	- SFT jobs:
	- Thinking: `2004401.pbs1`
	- Base: `2004402.pbs1`
	- HF checkpoint sync job:
	- `2004479.pbs1`

	## Monitoring Status

	The initial production SFT run was monitored past `checkpoint-500`.

	Observed stability:

	\| Run \| Step observed \| Memory plateau \| Error status \|
	\|---\|---:\|---:\|---\|
	\| Thinking \| `600+` \| ~`303GB` / `1.92TB` \| No OOM/NCCL/Traceback observed \|
	\| Base \| `590+` \| ~`294GB` / `1.92TB` \| No OOM/NCCL/Traceback observed \|

	Notes:

	- Both runs skipped GPU-side JSONL regeneration.
	- Both runs used the uploaded tokenized dataset.
	- W&B online logging was confirmed.
	- `checkpoint-500` was written locally and uploaded to Hugging Face Hub.
	- Base run showed `grad_norm=inf` in early logs while loss remained finite. This should be considered during downstream quality review.

	## Uploaded Checkpoint Contents

	Each `checkpoint-500/` directory on Hub contains:

	- `model.safetensors`
	- `config.json`
	- `generation_config.json`
	- tokenizer files
	- `trainer_state.json`
	- `training_args.bin`
	- RNG states
	- `scheduler.pt`

	The following large local training-state files are intentionally not uploaded to Hub:

	- `optimizer.bin`
	- `pytorch_model_fsdp.bin`

	They are retained in the ABCI experiment directory for local recovery/debugging.

	## Evaluation

	Full benchmark evaluation is not yet included in this model card.

	Planned gates for experiment `0399`:

	Primary math gates:

	- AIME 2024
	- AIME 2025
	- AIME 2026
	- MATH-500

	Regression gates:

	- LiveCodeBench
	- IFEval
	- MT-Bench

	Final-candidate-only gates:

	- GPQA Diamond
	- BBH
	- MMLU-Pro

	Do not treat this checkpoint as validated until these evaluations are complete.

	## Limitations

	- This is an intermediate/full-param SFT checkpoint from an active experiment.
	- The checkpoint is optimized for math reasoning style data and may regress in non-math tasks.
	- Training data may contain long chain-of-thought style solutions; generated outputs may be verbose.
	- Benchmark contamination mitigation was applied, but no contamination process is perfect.
	- The uploaded `checkpoint-500` is early in a longer training run and should not be interpreted as final model quality.
	- Safety alignment was not the primary target of this experiment.

	## Citation / Attribution

	Base models:

	```bibtex
	@misc{llmjp4,
	title = {LLM-jp-4 8B Models},
	author = {LLM-jp},
	year = {2026},
	url = {https://huggingface.co/llm-jp}
	}
	```

	Experiment tracking:

	- GitHub issue: https://github.com/llm-jp/experiments/issues/399
	- W&B project: https://wandb.ai/argo-lab/llmjp4-8b-teamvictory-sft-difficulty-20260629

	## Reproducibility Metadata

	Experiment ID: `0399`

	Experiment slug: `tv_sft`

	Canonical experiment directory:

	```text
	/groups/gcg51557/experiments/0399_tv_sft
	```

	Key manifests:

	```text
	/groups/gcg51557/experiments/0399_tv_sft/manifests/tokenized_sft_tv_valid_clean_llmjp4_8b.json
	/groups/gcg51557/experiments/0399_tv_sft/manifests/sft_stability_monitor_20260701.json
	/groups/gcg51557/experiments/0399_tv_sft/manifests/hf_checkpoint_sync_2004479.pbs1.json
	```

	Training configs:

	```text
	/groups/gcg51557/experiments/0399_tv_sft/configs/sft_full_thinking.yaml
	/groups/gcg51557/experiments/0399_tv_sft/configs/sft_full_base.yaml
	```