88 lines
2.9 KiB
Markdown
88 lines
2.9 KiB
Markdown
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---
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license: other
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license_name: nvidia-open-model-license
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license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
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library_name: transformers
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pipeline_tag: text-generation
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base_model: nvidia/Cosmos-Reason2-2B
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tags:
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- transformers
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- safetensors
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- qwen3
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- cosmos
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- nvidia
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- text-generation
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- text-only
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---
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# Cosmos-Reason2-2B Text-Only
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This is a text-only extraction of [`nvidia/Cosmos-Reason2-2B`](https://huggingface.co/nvidia/Cosmos-Reason2-2B).
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Built on NVIDIA Cosmos.
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The original model is a Qwen3-VL/Cosmos vision-language model. This repository keeps the language backbone and `lm_head`, removes the vision tower and projector weights, and saves the result as a standalone Hugging Face `Qwen3ForCausalLM` checkpoint.
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## What Changed
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- Source model: `nvidia/Cosmos-Reason2-2B`
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- Output architecture: `Qwen3ForCausalLM`
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- Output `model_type`: `qwen3`
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- Kept tensors: 311
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- Dropped tensors: 315
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- Removed weight prefixes include `model.visual.*` and other multimodal components
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- Output weights: `model.safetensors`
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The original nested text config used `qwen3_vl_text`. It was converted to a `qwen3` CausalLM-compatible config because the tested Transformers environment did not expose `qwen3_vl_text` through `AutoModelForCausalLM`.
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## Validation
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Validated locally with:
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- `torch 2.12.1+cpu`
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- `transformers 5.12.1`
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- `safetensors 0.8.0`
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Checks performed:
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- `AutoConfig.from_pretrained(...)` loads as `Qwen3Config`
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- `AutoTokenizer.from_pretrained(...)` loads as `Qwen2Tokenizer`
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- `AutoModelForCausalLM.from_pretrained(...)` loads as `Qwen3ForCausalLM`
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- Forward pass succeeds on a short text prompt
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- Output logits shape: `(1, 7, 151936)`
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- No `visual`, `vision`, `projector`, or `language_model` tensor names remain in the exported checkpoint
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "sasa2000/cosmos-reason2-2b-text-only"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto",
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)
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inputs = tokenizer("Explain why objects fall toward Earth.", return_tensors="pt").to(model.device)
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with torch.no_grad():
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output_ids = model.generate(**inputs, max_new_tokens=128)
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print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
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```
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## Limitations
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This checkpoint is text-only. It does not include the original vision tower, video/image processor, or multimodal projector weights. Image and video inputs are not supported.
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This is an unofficial derived checkpoint and is not released by NVIDIA.
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## License
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The source model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). Use of this derived checkpoint must comply with the original model license and any applicable terms.
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Licensed by NVIDIA Corporation under the NVIDIA Open Model License.
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