Instructions to use sasa2000/Alpamayo-R1-10B-Text-Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sasa2000/Alpamayo-R1-10B-Text-Only with Transformers:

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

pipe = pipeline("text-generation", model="sasa2000/Alpamayo-R1-10B-Text-Only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sasa2000/Alpamayo-R1-10B-Text-Only")
model = AutoModelForCausalLM.from_pretrained("sasa2000/Alpamayo-R1-10B-Text-Only")
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 sasa2000/Alpamayo-R1-10B-Text-Only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sasa2000/Alpamayo-R1-10B-Text-Only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sasa2000/Alpamayo-R1-10B-Text-Only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sasa2000/Alpamayo-R1-10B-Text-Only

SGLang

How to use sasa2000/Alpamayo-R1-10B-Text-Only 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 "sasa2000/Alpamayo-R1-10B-Text-Only" \
    --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": "sasa2000/Alpamayo-R1-10B-Text-Only",
		"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 "sasa2000/Alpamayo-R1-10B-Text-Only" \
        --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": "sasa2000/Alpamayo-R1-10B-Text-Only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sasa2000/Alpamayo-R1-10B-Text-Only with Docker Model Runner:
```
docker model run hf.co/sasa2000/Alpamayo-R1-10B-Text-Only
```

Alpamayo-R1-10B-Text-Only / README.md

sasa2000

Fix tokenizer config for GGUF conversion

de8b824 verified 10 days ago

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	---
	license: other
	library_name: transformers
	pipeline_tag: text-generation
	base_model: nvidia/Alpamayo-R1-10B
	tags:
	- transformers
	- safetensors
	- qwen3
	- alpamayo
	- nvidia
	- text-generation
	- text-only
	---

	# Alpamayo-R1-10B Text-Only

	This is a text-only extraction of [`nvidia/Alpamayo-R1-10B`](https://huggingface.co/nvidia/Alpamayo-R1-10B), also known as Alpamayo 1.

	The original checkpoint is a vision-language-action model with:

	- a Qwen3-VL/Cosmos-style VLM backbone,
	- a vision tower,
	- a diffusion/action expert,
	- trajectory/action projection modules.

	This repository keeps only the language backbone from `vlm.model.language_model.*` plus `vlm.lm_head.weight`, and saves it as a standalone Hugging Face `Qwen3ForCausalLM` checkpoint.

	## What Changed

	- Source model: `nvidia/Alpamayo-R1-10B`
	- Output architecture: `Qwen3ForCausalLM`
	- Output `model_type`: `qwen3`
	- Kept tensors: 399
	- Dropped tensors: 767
	- Output weights: 4 safetensors shards
	- Removed components include `vlm.model.visual.`, `expert.`, `action_in_proj.`, `action_out_proj.`, and `action_space.*`

	The source repository does not include tokenizer files. The tokenizer here is based on `Qwen/Qwen3-VL-8B-Instruct` and extended with Alpamayo placeholder special tokens up to the model vocabulary size `155697`.
	For GGUF conversion compatibility, the tokenizer config stores the Alpamayo placeholder tokens in `additional_special_tokens`, and the BPE `vocab.json` / `merges.txt` files are included alongside `tokenizer.json`.

	## Validation

	Validated locally with:

	- `torch 2.12.1+cpu`
	- `transformers 5.12.1`
	- `safetensors 0.8.0`

	Checks performed:

	- `AutoConfig.from_pretrained(...)` loads as `Qwen3Config`
	- `AutoTokenizer.from_pretrained(...)` loads as `Qwen2Tokenizer`
	- tokenizer length is `155697`
	- `AutoTokenizer.from_pretrained(...)` loads without `extra_special_tokens` compatibility errors in current Transformers
	- `AutoModelForCausalLM.from_pretrained(...)` loads as `Qwen3ForCausalLM`
	- Forward pass succeeds on a short text prompt
	- Output logits shape: `(1, 10, 155697)`
	- No `visual`, `vision`, `projector`, `language_model`, `expert`, `action_`, or `vlm.` tensor names remain in the exported checkpoint

	## Usage

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_dir = "path/to/alpamayo_r1_10b_text_only"

	tokenizer = AutoTokenizer.from_pretrained(model_dir, fix_mistral_regex=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_dir,
	torch_dtype="auto",
	device_map="auto",
	)

	inputs = tokenizer("Explain a safe driving decision at a busy intersection.", return_tensors="pt").to(model.device)
	with torch.no_grad():
	output_ids = model.generate(**inputs, max_new_tokens=128)

	print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
	```

	## Limitations

	This checkpoint is text-only. It does not include the original vision tower, robotics/action expert, diffusion trajectory decoder, multimodal processors, or trajectory decoding logic.

	This is an unofficial derived checkpoint and is not released by NVIDIA.

	## License

	The source model states that its weights are released under a non-commercial license. Use of this derived checkpoint must comply with the original model license and any applicable terms.