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Model: srs6901/Vikras-MixP
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Vikra-HCT-YeAM-3_3.2_QweLLa-1.7B/README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-1.7B-Base
+---
+
+# Vikra-HCT-YeAM-3_3.2_QweLLa-1.7B
+
+HCT architecture release. YeAM (Yet Another Merge) implementation invariant.
+
+## What it is
+
+A compact 1.7B-class checkpoint 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.
+
+## Notes for this checkpoint
+
+Compared to other YeAM/HCT merges, this checkpoint additionally applies a targeted merge on Attention projection weights.
+
+Observed behavior tends to include characteristic Llama-like traits:
+- More Llama-style conversation patterns.
+- More consistent formatting.
+- Stronger RLHF-like refusal/priority behaviors.
+- Reasoning / chain-of-thought style output in the model's full native format is expected to work.
+
+At the same time, most Qwen3 behavior should theoretically remain, but due to knowledge/logic injection from the Llama side, some Qwen-specific properties may be partially degraded or inconsistent.
+
+Repetition / looping:
+- There is no universally perfect sampling configuration.
+- At higher temperature, without a repetition-style penalty, the model may enter repetition loops.
+- Pay special attention to repetition-related controls (e.g. repetition penalty / presence penalty) if you observe cycling.
+
+Do not ask the model who created it.
+In this specific merge, it may oscillate between incompatible parents (Alibaba vs Meta”), fail to settle, and get stuck in a sad loop.
+
+## Usage (Transformers)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+m = "/path/to/Vikra-HCT-YeAM-3_3.2_QweLLa-1.7B"
+
+tok = AutoTokenizer.from_pretrained(m, use_fast=True)
+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 f16 --outfile model.f16.gguf
+/path/to/llama.cpp/build/bin/llama-quantize model.f16.gguf model.Q8_0.gguf Q8_0
+```