SupraLabs/Supra-1.5-50M-instruct-exp-gguf overview

---

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

GGUF quantizations of SupraLabs/Supra-1.5-50M-instruct-exp, an experimental 50M-parameter instruction-tuned model by 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

# 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

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

Python (llama-cpp-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.

---

🧠 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 | Pretrained base (v1.5) |

| Supra-1.5-50M-instruct-exp | fp weights |

| Supra-50M-Base | v1.0 pretrained base |

| Supra-50M-Instruct | v1.0 instruct model |

| Supra-50M-Reasoning | Chain-of-thought reasoning variant |

---

📄 License

Released under the Apache 2.0 License.

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© SupraLabs 2026 — Project Chimera

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Credit goes to @QyrouNnet-AI