---
license: gemma
library_name: transformers
pipeline_tag: text-generation
base_model: google/gemma-3-1b-pt
---

# Vikra-HCT-YeAM-PhiMma-1B
Gemma-3-1b-pt + Microsoft_phi-2
HCT architecture release. YeAM (Yet Another Merge) implementation invariant.

## What it is

A compact 1B-class model produced via HCT-compatible merging.
The checkpoint is published in standard Hugging Face format (safetensors + index).

## YeAM summary

YeAM performs a controlled merge in a real 4D geometric formulation with ray-intersection alignment in parameter space.
It also supports targeted knowledge injection (distillation-style) into a chosen model while remaining HF-compatible.

## Usage (Transformers)

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

m = "/path/to/Vikra-HCT-YeAM-PhiMma-1B"

tok = AutoTokenizer.from_pretrained(m, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    m,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
).eval()

inputs = tok("Hello!", return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128)
print(tok.decode(out[0], skip_special_tokens=True))
```

## GGUF

Convert and quantize with llama.cpp (example):

```bash
python3 /path/to/llama.cpp/convert_hf_to_gguf.py /path/to/model --outtype bf16 --outfile model.bf16.gguf
/path/to/llama.cpp/build/bin/llama-quantize model.bf16.gguf model.Q6_K.gguf Q6_K
```