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sasa2000/Alpamayo-R1-10B-Text-Only
Alpamayo-R1-10B-Text-Only is a text generation model from sasa2000. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
This is a text-only extraction of nvidia/Alpamayo-R1-10B, also known as Alpamayo 1.
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From the Hugging Face model README
This is a text-only extraction of nvidia/Alpamayo-R1-10B, also known as Alpamayo 1.
The original checkpoint is a vision-language-action model with:
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.
nvidia/Alpamayo-R1-10BQwen3ForCausalLMmodel_type: qwen3vlm.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.
Validated locally with:
torch 2.12.1+cputransformers 5.12.1safetensors 0.8.0Checks performed:
AutoConfig.from_pretrained(...) loads as Qwen3ConfigAutoTokenizer.from_pretrained(...) loads as Qwen2Tokenizer155697AutoTokenizer.from_pretrained(...) loads without extra_special_tokens compatibility errors in current TransformersAutoModelForCausalLM.from_pretrained(...) loads as Qwen3ForCausalLM(1, 10, 155697)visual, vision, projector, language_model, expert, action_*, or vlm.* tensor names remain in the exported checkpointimport 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))
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.
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.