Supra-1.5 Instruct • Experimental Chat Tune — GGUF

👁 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.

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📦 Available Quantizations

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

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🚀 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."))

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💬 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:

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🏆 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.

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🧠 Model Architecture

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🔗 Related Models

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📄 License

Released under the Apache 2.0 License.

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© SupraLabs 2026 — Project Chimera

Base model

SupraLabs/Supra-50M-Base

SupraLabs/Supra-1.5-50M-Base-exp

SupraLabs/Supra-1.5-50M-Instruct-exp

(10)

All Supra-1.5-50M models • 3 items • Updated 5 days ago • 7