README.md

---
language:
- en
- fr
- it
- de
- es
license: apache-2.0
tags:
- mixtral
- moe
- mixture-of-experts
- merge
- chimera
- klyrone
- instruct
- text-generation
base_model:
- mistralai/Mixtral-8x7B-v0.1
- mistralai/Mixtral-8x7B-Instruct-v0.1
base_model_relation: merge
model_type: mixtral
pipeline_tag: text-generation
inference: false
---

# Chimera 47B

**Klyrone F.Z.E.** · March 2026 · Apache 2.0

Chimera 47B is a 46.7B parameter Mixture-of-Experts language model built using Klyrone's MoE assembly framework. It is constructed from Mixtral-8x7B-v0.1 and Mixtral-8x7B-Instruct-v0.1 — combining the base model's knowledge with the instruct model's capabilities — without any additional training. With 8 experts and top-2 routing, only 12.9B parameters are active per token, enabling fast inference at 154 tokens/second on H200 hardware.

A technical paper detailing the methodology is forthcoming.

---

## Key Numbers

| | |
|---|---|
| Total Parameters | 46.7 B |
| Active / Token | 12.9 B |
| Architecture | MoE · 8 experts · top-2 routing |
| Context Length | 32,768 tokens |
| Generation Speed | 154 t/s · H200 |
| Prompt Processing | 878 t/s · H200 |
| Quantization | Q5_K_M · 5.69 BPW |
| File Size | 30.95 GB GGUF |
| License | Apache 2.0 |

---

## Capabilities

- ✅ Instruction following — multi-turn conversational coherence
- ✅ Code generation — correct, edge-case-aware output
- ✅ Creative writing — long-form prose and poetry
- ✅ Factual reasoning — physics, mathematics, general knowledge
- ✅ Consumer-grade deployment — fits accessible GPU budgets at Q5_K_M

> Formal benchmark results (MMLU, HellaSwag, ARC-Challenge, GSM8K) in progress.

---

## About the Approach

Klyrone's MoE assembly framework constructs high-performance models by composing expert sub-networks from compatible source models — without full retraining. The expert FFN weights (w1, w2, w3) from Mixtral-8x7B-Instruct are transplanted into the Mixtral-8x7B-v0.1 base, preserving routing coherence while inheriting the instruction-tuned capabilities of the donor model.

For enterprise licensing or research collaboration, contact **research@klyrone.com**

---

## Usage

### llama.cpp

```bash
./llama-server \
  -m Chimera-47B-Q5_K_M.gguf \
  -ngl 99 \
  --ctx-size 32768 \
  --port 8080
```

Or for direct CLI inference:

```bash
./llama-cli \
  -m Chimera-47B-Q5_K_M.gguf \
  -p "You are a helpful assistant." \
  --ctx-size 32768 \
  -ngl 99 \
  -n 512
```

### llama-cpp-python

```python
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="klyrone/Chimera",
    filename="Chimera-47B-Q5_K_M.gguf",
    n_gpu_layers=99,
    n_ctx=4096,
    verbose=False
)

output = llm(
    "You are a helpful assistant.\n\nExplain the difference between supervised and unsupervised learning.",
    max_tokens=512,
    stop=["</s>"]
)
print(output["choices"][0]["text"])
```

### Ollama

```bash
ollama run hf.co/klyrone/Chimera
```

> **Note:** This model is distributed as a GGUF file. Native Transformers loading (`AutoModelForCausalLM`) is not supported directly — use llama.cpp, llama-cpp-python, or Ollama for inference.

---

## Hardware Requirements

| Quantization | VRAM Required | Recommended Hardware |
|---|---|---|
| Q5_K_M (this file) | ~34 GB | A40, A100, 2× 3090/4090 |
| Q4_K_M | ~27 GB | 3090/4090, A6000 |
| Q3_K_M | ~22 GB | 24 GB consumer GPU |

---

## Limitations

- Router fine-tuning not yet applied — a short gate re-alignment is expected to yield marginal quality gains
- No independent safety evaluation conducted — not recommended for unsupervised public-facing deployment
- Benchmark results pending publication
- STEM-heavy benchmarks (abstract algebra, HS math) may underperform relative to general capability, as mathematical knowledge is distributed across attention layers rather than expert FFNs

---

## Citation

```bibtex
@misc{chimera47b2026,
  title        = {Chimera 47B},
  author       = {{Klyrone F.Z.E.}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/klyrone/Chimera}}
}
```

---

*Chimera 47B · Klyrone F.Z.E. · Apache 2.0 · A technical paper on the MoE assembly technique is forthcoming.*