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 / checkpoint-1000 /tokenizer_config.json

argo11

0399 upload checkpoint-1000

03415e6 verified about 2 months ago

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1.99 kB

	{
	"add_prefix_space": null,
	"auto_map": {
	"AutoTokenizer": [
	"llmjp4_tokenizer.Llmjp4Tokenizer",
	null
	]
	},
	"backend": "tokenizers",
	"bos_token": "<\|startoftext\|>",
	"clean_up_tokenization_spaces": false,
	"cls_token": "<\|cls\|>",
	"eod_token": "<\|eod\|>",
	"eos_token": "<\|return\|>",
	"extra_ids": 0,
	"is_local": false,
	"local_files_only": false,
	"mask_token": "<\|mask\|>",
	"model_max_length": 1000000000000000019884624838656,
	"model_specific_special_tokens": {
	"eod_token": "<\|eod\|>"
	},
	"pad_token": "<\|endoftext\|>",
	"response_schema": {
	"properties": {
	"content": {
	"type": "string",
	"x-regex": "<\\\|channel\\\|>final<\\\|message\\\|>(.*?)(?:<\\\|end\\\|>\|<\\\|return\\\|>\|$)"
	},
	"role": {
	"const": "assistant"
	},
	"thinking": {
	"type": "string",
	"x-regex": "<\\\|channel\\\|>analysis<\\\|message\\\|>(.*?)<\\\|end\\\|>"
	},
	"tool_calls": {
	"items": {
	"properties": {
	"function": {
	"properties": {
	"arguments": {
	"additionalProperties": {
	"type": "any"
	},
	"type": "object",
	"x-parser": "json",
	"x-regex": "<\\\|message\\\|>(.*)"
	},
	"name": {
	"type": "string",
	"x-regex": "^to=functions\\.(\\w+)"
	}
	},
	"type": "object"
	},
	"type": {
	"const": "function"
	}
	},
	"type": "object"
	},
	"type": "array",
	"x-regex-iterator": "<\\\|channel\\\|>commentary (to=functions\\..?<\\\|message\\\|>.?)(?:<\\\|call\\\|>\|$)"
	}
	},
	"type": "object"
	},
	"sep_token": "<\|sep\|>",
	"sp_model_kwargs": {},
	"tokenizer_class": "Llmjp4Tokenizer",
	"unk_token": "<\|unk\|>",
	"use_default_system_prompt": false
	}