---
license: apache-2.0
language:
- en
base_model: SupraLabs/Supra-1.5-50M-instruct-exp
pipeline_tag: text-generation
tags:
- supra
- chimera
- project-chimera
- gguf
- quantized
- instruct
- conversational
- QnA
- GPT
- CPU
- tiny
- SLM
- open
- open-source
- 50M
- llama
---

<h1 align="center">Supra-1.5 Instruct • Experimental Chat Tune — GGUF</h1>

![Supra-1.5 Instruct](https://cdn-uploads.huggingface.co/production/uploads/68a5d0966d33a07f8aad2e51/GUYqhtlWoCAUnRLR1zEOX.png)

GGUF quantizations of [SupraLabs/Supra-1.5-50M-instruct-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-instruct-exp), an experimental 50M-parameter instruction-tuned model by [SupraLabs](https://huggingface.co/SupraLabs), part of **Project Chimera**.

Run it entirely on CPU, low-VRAM GPUs, or embedded hardware. No cloud required.

> **Note:** This is an experimental model. Do not use in production.

---

## 📦 Available Quantizations

| Bits | Quantization | Size |
|:--|:--|:--|
| 1-bit | `Q1_0` | 19.6 MB |
| 1-bit | `TQ1_0` | 25.1 MB |
| 2-bit | `Q2_K` | 28.8 MB |
| 2-bit | `TQ2_0` | 26.4 MB |
| 3-bit | `IQ3_S` | 31 MB |
| 3-bit | `Q3_K_S` | 31 MB |
| 3-bit | `IQ3_M` | 31.7 MB |
| 3-bit | `Q3_K_M` | 32.7 MB |
| 3-bit | `Q3_K_L` | 33.8 MB |
| 4-bit | `IQ4_XS` | 33.8 MB |
| 4-bit | `Q4_K_S` | 35.7 MB |
| 4-bit | `IQ4_NL` | 34.7 MB |
| 4-bit | `Q4_0` | 34.5 MB |
| 4-bit | `Q4_1` | 36.8 MB |
| 4-bit | `Q4_K_M` | 37.4 MB |
| 5-bit | `Q5_K_S` | 39.5 MB |
| 5-bit | `Q5_0` | 39 MB |
| 5-bit | `Q5_1` | 41.2 MB |
| 5-bit | `Q5_K_M` | 41 MB |
| 6-bit | `Q6_K` | 45.8 MB |
| 8-bit | `Q8_0` | 56.2 MB |
| 16-bit | `BF16` | 105 MB |
| 16-bit | `F16` | 105 MB |
| 32-bit | `F32` | 208 MB |

> **`Q4_K_M`** — Usable, not recommended unless device is compute-constrained.  
> **`Q8_0`** — Perfect size/performance!.  
> **`Q2_K`** — ultra-constrained devices (not reccomended!).

---

## 🚀 Quick Start

### llama.cpp

```bash
# Download
huggingface-cli download SupraLabs/Supra-1.5-50M-instruct-exp-gguf \
  --include "*.Q4_K_M.gguf" \
  --local-dir ./

# Run
./llama-cli \
  -m supra-1.5-50m-instruct-exp-Q4_K_M.gguf \
  -p "### Instruction:\nWhat is machine learning?\n\n### Response:\n" \
  -n 256 \
  --temp 0.7 \
  --repeat-penalty 1.15
```

### Ollama

```bash
ollama run hf.co/SupraLabs/Supra-1.5-50M-instruct-exp-gguf:Q4_K_M
```

### Python (llama-cpp-python)

```python
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="SupraLabs/Supra-1.5-50M-instruct-exp-gguf",
    filename="*Q4_K_M.gguf",
    n_ctx=1024,
    verbose=False,
)

def chat(instruction: str, input_text: str = "") -> str:
    if input_text.strip():
        prompt = (
            "Below is an instruction that describes a task, paired with an input "
            "that provides further context. Write a response that appropriately "
            "completes the request.\n\n"
            f"### Instruction:\n{instruction}\n\n"
            f"### Input:\n{input_text}\n\n"
            "### Response:\n"
        )
    else:
        prompt = (
            "Below is an instruction that describes a task. Write a response that "
            "appropriately completes the request.\n\n"
            f"### Instruction:\n{instruction}\n\n"
            "### Response:\n"
        )
    output = llm(prompt, max_tokens=256, temperature=0.7, top_k=50, top_p=0.9, repeat_penalty=1.15)
    return output["choices"][0]["text"].strip()

print(chat("Explain what artificial intelligence is."))
```

---

## 💬 Prompt Format

This model uses the **Alpaca Chat Format**:

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
```

With optional input:

```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
```

---

## 🏆 Benchmarks

Supra-1.5-50M-instruct-exp achieves superior performance within the 50M-parameter class, with a consistent **BLiMP score of 67.4**.

Key findings from evaluation:
- **Scientific/factual tasks** perform best under raw inference (no normalization)
- **Math and logical reasoning** benefit from normalized inference
- **Top syntactic categories**: structural dependency tracking, complex clausal configurations, and subtle syntactic error detection — performing at near-flawless precision
- **Hardest categories**: advanced binding phenomena and morphological agreement edge cases, reflecting known limits of 50M-class architectures

> For full benchmark charts and BLiMP probe analysis, see the [base model card](https://huggingface.co/SupraLabs/Supra-1.5-50M-instruct-exp).

---

## 🧠 Model Architecture

| Property | Value |
|:--|:--|
| Architecture | Llama (decoder-only) |
| Parameters | ~50M |
| Vocabulary | 32,000 (custom BPE) |
| Context length | 5,120 tokens |
| Hidden size | 512 |
| Layers | 12 |
| Attention heads | 8 (GQA: 4 KV heads) |
| Base model | SupraLabs/Supra-1.5-50M-Base-exp |
| License | Apache 2.0 |

---

## 🔗 Related Models

| Model | Description |
|:--|:--|
| [Supra-1.5-50M-Base-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-Base-exp) | Pretrained base (v1.5) |
| [Supra-1.5-50M-instruct-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-instruct-exp) | fp weights |
| [Supra-50M-Base](https://huggingface.co/SupraLabs/Supra-50M-Base) | v1.0 pretrained base |
| [Supra-50M-Instruct](https://huggingface.co/SupraLabs/Supra-50M-Instruct) | v1.0 instruct model |
| [Supra-50M-Reasoning](https://huggingface.co/SupraLabs/Supra-50M-Reasoning) | Chain-of-thought reasoning variant |

---

## 📄 License

Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).

---

*© SupraLabs 2026 — Project Chimera*