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Model: SupraLabs/Supra-1.5-50M-instruct-exp-gguf Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model: SupraLabs/Supra-1.5-50M-instruct-exp
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pipeline_tag: text-generation
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tags:
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- supra
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- chimera
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- project-chimera
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- gguf
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- quantized
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- instruct
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- conversational
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- QnA
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- GPT
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- CPU
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- tiny
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- SLM
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- open
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- open-source
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- 50M
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- llama
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---
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<h1 align="center">Supra-1.5 Instruct • Experimental Chat Tune — GGUF</h1>
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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**.
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Run it entirely on CPU, low-VRAM GPUs, or embedded hardware. No cloud required.
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> **Note:** This is an experimental model. Do not use in production.
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---
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## 📦 Available Quantizations
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| Bits | Quantization | Size |
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|:--|:--|:--|
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| 1-bit | `Q1_0` | 19.6 MB |
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| 1-bit | `TQ1_0` | 25.1 MB |
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| 2-bit | `Q2_K` | 28.8 MB |
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| 2-bit | `TQ2_0` | 26.4 MB |
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| 3-bit | `IQ3_S` | 31 MB |
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| 3-bit | `Q3_K_S` | 31 MB |
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| 3-bit | `IQ3_M` | 31.7 MB |
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| 3-bit | `Q3_K_M` | 32.7 MB |
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| 3-bit | `Q3_K_L` | 33.8 MB |
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| 4-bit | `IQ4_XS` | 33.8 MB |
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| 4-bit | `Q4_K_S` | 35.7 MB |
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| 4-bit | `IQ4_NL` | 34.7 MB |
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| 4-bit | `Q4_0` | 34.5 MB |
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| 4-bit | `Q4_1` | 36.8 MB |
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| 4-bit | `Q4_K_M` | 37.4 MB |
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| 5-bit | `Q5_K_S` | 39.5 MB |
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| 5-bit | `Q5_0` | 39 MB |
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| 5-bit | `Q5_1` | 41.2 MB |
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| 5-bit | `Q5_K_M` | 41 MB |
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| 6-bit | `Q6_K` | 45.8 MB |
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| 8-bit | `Q8_0` | 56.2 MB |
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| 16-bit | `BF16` | 105 MB |
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| 16-bit | `F16` | 105 MB |
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| 32-bit | `F32` | 208 MB |
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> **`Q4_K_M`** — Usable, not recommended unless device is compute-constrained.
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> **`Q8_0`** — Perfect size/performance!.
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> **`Q2_K`** — ultra-constrained devices (not reccomended!).
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---
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## 🚀 Quick Start
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### llama.cpp
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```bash
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# Download
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huggingface-cli download SupraLabs/Supra-1.5-50M-instruct-exp-gguf \
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--include "*.Q4_K_M.gguf" \
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--local-dir ./
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# Run
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./llama-cli \
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-m supra-1.5-50m-instruct-exp-Q4_K_M.gguf \
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-p "### Instruction:\nWhat is machine learning?\n\n### Response:\n" \
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-n 256 \
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--temp 0.7 \
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--repeat-penalty 1.15
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```
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### Ollama
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```bash
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ollama run hf.co/SupraLabs/Supra-1.5-50M-instruct-exp-gguf:Q4_K_M
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```
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### Python (llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="SupraLabs/Supra-1.5-50M-instruct-exp-gguf",
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filename="*Q4_K_M.gguf",
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n_ctx=1024,
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verbose=False,
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)
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def chat(instruction: str, input_text: str = "") -> str:
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if input_text.strip():
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prompt = (
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"Below is an instruction that describes a task, paired with an input "
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"that provides further context. Write a response that appropriately "
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"completes the request.\n\n"
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f"### Instruction:\n{instruction}\n\n"
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f"### Input:\n{input_text}\n\n"
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"### Response:\n"
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)
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else:
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prompt = (
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"Below is an instruction that describes a task. Write a response that "
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"appropriately completes the request.\n\n"
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f"### Instruction:\n{instruction}\n\n"
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"### Response:\n"
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)
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output = llm(prompt, max_tokens=256, temperature=0.7, top_k=50, top_p=0.9, repeat_penalty=1.15)
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return output["choices"][0]["text"].strip()
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print(chat("Explain what artificial intelligence is."))
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```
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---
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## 💬 Prompt Format
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This model uses the **Alpaca Chat Format**:
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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```
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With optional input:
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Input:
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{input}
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### Response:
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```
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---
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## 🏆 Benchmarks
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Supra-1.5-50M-instruct-exp achieves superior performance within the 50M-parameter class, with a consistent **BLiMP score of 67.4**.
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Key findings from evaluation:
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- **Scientific/factual tasks** perform best under raw inference (no normalization)
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- **Math and logical reasoning** benefit from normalized inference
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- **Top syntactic categories**: structural dependency tracking, complex clausal configurations, and subtle syntactic error detection — performing at near-flawless precision
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- **Hardest categories**: advanced binding phenomena and morphological agreement edge cases, reflecting known limits of 50M-class architectures
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> For full benchmark charts and BLiMP probe analysis, see the [base model card](https://huggingface.co/SupraLabs/Supra-1.5-50M-instruct-exp).
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---
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## 🧠 Model Architecture
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| Property | Value |
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| Architecture | Llama (decoder-only) |
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| Parameters | ~50M |
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| Vocabulary | 32,000 (custom BPE) |
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| Context length | 5,120 tokens |
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| Hidden size | 512 |
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| Layers | 12 |
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| Attention heads | 8 (GQA: 4 KV heads) |
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| Base model | SupraLabs/Supra-1.5-50M-Base-exp |
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| License | Apache 2.0 |
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---
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## 🔗 Related Models
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| Model | Description |
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| [Supra-1.5-50M-Base-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-Base-exp) | Pretrained base (v1.5) |
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| [Supra-1.5-50M-instruct-exp](https://huggingface.co/SupraLabs/Supra-1.5-50M-instruct-exp) | fp weights |
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| [Supra-50M-Base](https://huggingface.co/SupraLabs/Supra-50M-Base) | v1.0 pretrained base |
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| [Supra-50M-Instruct](https://huggingface.co/SupraLabs/Supra-50M-Instruct) | v1.0 instruct model |
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| [Supra-50M-Reasoning](https://huggingface.co/SupraLabs/Supra-50M-Reasoning) | Chain-of-thought reasoning variant |
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---
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## 📄 License
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Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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---
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*© SupraLabs 2026 — Project Chimera*
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