51 lines
1.4 KiB
Markdown
51 lines
1.4 KiB
Markdown
---
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license: gemma
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library_name: transformers
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pipeline_tag: text-generation
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base_model: google/gemma-3-1b-pt
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---
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# Vikra-HCT-YeAM-LLaGemma-1B
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Llama-3.2-1B-Instruct + Gemma-3-1b-pt
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HCT architecture release. YeAM (Yet Another Merge) implementation invariant.
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## What it is
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A compact 1B-class model produced via HCT-compatible merging.
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The checkpoint is published in standard Hugging Face format (safetensors + index).
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## YeAM summary
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YeAM performs a controlled merge in a real 4D geometric formulation with ray-intersection alignment in parameter space.
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It also supports targeted knowledge injection (distillation-style) into a chosen model while remaining HF-compatible.
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## Usage (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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m = "/path/to/Vikra-HCT-YeAM-LLaGemma-1B"
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tok = AutoTokenizer.from_pretrained(m, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(
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m,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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).eval()
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inputs = tok("Hello!", return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=128)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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## GGUF
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Convert and quantize with llama.cpp (example):
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```bash
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python3 /path/to/llama.cpp/convert_hf_to_gguf.py /path/to/model --outtype bf16 --outfile model.bf16.gguf
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/path/to/llama.cpp/build/bin/llama-quantize model.bf16.gguf model.Q6_K.gguf Q6_K
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```
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