Instructions to use NANI-Nithin/manaca-1b-base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NANI-Nithin/manaca-1b-base-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/manaca-1b-base-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/manaca-1b-base-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/manaca-1b-base-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/manaca-1b-base-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use NANI-Nithin/manaca-1b-base-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/manaca-1b-base-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.manaca-1b-base-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

NANI-Nithin commited on 6 days ago

Commit

12eb11e

verified ·

1 Parent(s): 0748ea6

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	@@ -0,0 +1,82 @@

+---
+base_model: menezesbruno/manaca-1b-base
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+  - gguf
+  - llama.cpp
+  - quantized
+  - imatrix
+---
+# manaca-1b-base GGUF
+GGUF quantizations of [menezesbruno/manaca-1b-base](https://huggingface.co/menezesbruno/manaca-1b-base), covering 30 files (27.0 GB total).
+## Files
+| File | Quant | Size | Notes |
+|---|---|---:|---|
+| [manaca-1b-base-BF16.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-BF16.gguf) | `BF16` | 3.21 GB | Full precision source. Every quant below is cut from this file. |
+| [manaca-1b-base-Q8_0.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q8_0.gguf) | `Q8_0` | 1.71 GB | Effectively lossless. Use when disk and RAM are not the constraint. |
+| [manaca-1b-base-Q6_K.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q6_K.gguf) | `Q6_K` | 1.32 GB | Near-lossless; the last stop before quality becomes measurable. |
+| [manaca-1b-base-Q5_K_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q5_K_M.gguf) | `Q5_K_M` | 1.15 GB | Very good quality, noticeably smaller than Q6_K. |
+| [manaca-1b-base-Q5_K_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q5_K_S.gguf) | `Q5_K_S` | 1.12 GB | Slightly smaller than Q5_K_M for a slight quality cost. |
+| [manaca-1b-base-Q5_1.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q5_1.gguf) | `Q5_1` | 1.22 GB | Legacy. Prefer Q5_K_M. |
+| [manaca-1b-base-Q5_0.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q5_0.gguf) | `Q5_0` | 1.12 GB | Legacy. Prefer Q5_K_M. |
+| [manaca-1b-base-Q4_K_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q4_K_M.gguf) | `Q4_K_M` | 0.99 GB | The usual default. Best quality-per-byte for most people. |
+| [manaca-1b-base-Q4_K_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q4_K_S.gguf) | `Q4_K_S` | 0.94 GB | A little smaller than Q4_K_M, a little worse. |
+| [manaca-1b-base-IQ4_NL.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ4_NL.gguf) | `IQ4_NL` | 0.94 GB | Non-linear 4-bit; good on hardware without fast K-quant kernels. |
+| [manaca-1b-base-IQ4_XS.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ4_XS.gguf) | `IQ4_XS` | 0.90 GB | Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size. |
+| [manaca-1b-base-Q4_1.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q4_1.gguf) | `Q4_1` | 1.03 GB | Legacy. Prefer Q4_K_M. |
+| [manaca-1b-base-Q4_0.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q4_0.gguf) | `Q4_0` | 0.94 GB | Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this. |
+| [manaca-1b-base-Q3_K_L.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q3_K_L.gguf) | `Q3_K_L` | 0.87 GB | Small, with real quality loss. Usable when RAM is tight. |
+| [manaca-1b-base-Q3_K_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q3_K_M.gguf) | `Q3_K_M` | 0.81 GB | Smaller again; noticeable degradation. |
+| [manaca-1b-base-IQ3_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ3_M.gguf) | `IQ3_M` | 0.76 GB | Strong at ~3.7bpw, clearly better than Q3_K_M. |
+| [manaca-1b-base-IQ3_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ3_S.gguf) | `IQ3_S` | 0.74 GB | Slightly smaller than IQ3_M. |
+| [manaca-1b-base-Q3_K_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q3_K_S.gguf) | `Q3_K_S` | 0.74 GB | Aggressive. Prefer IQ3_M at a similar size. |
+| [manaca-1b-base-IQ3_XS.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ3_XS.gguf) | `IQ3_XS` | 0.71 GB | Aggressive but coherent. |
+| [manaca-1b-base-IQ3_XXS.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ3_XXS.gguf) | `IQ3_XXS` | 0.66 GB | Very aggressive; imatrix carries it. |
+| [manaca-1b-base-Q2_K.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q2_K.gguf) | `Q2_K` | 0.64 GB | Very small, heavily degraded. For experimentation. |
+| [manaca-1b-base-IQ2_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ2_M.gguf) | `IQ2_M` | 0.60 GB | The smallest size most people find usable. |
+| [manaca-1b-base-Q2_K_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q2_K_S.gguf) | `Q2_K_S` | 0.61 GB | Smaller than Q2_K, requires the imatrix. |
+| [manaca-1b-base-IQ2_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ2_S.gguf) | `IQ2_S` | 0.56 GB | Below the usual usability line. |
+| [manaca-1b-base-IQ2_XS.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ2_XS.gguf) | `IQ2_XS` | 0.53 GB | Experimental. |
+| [manaca-1b-base-IQ2_XXS.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ2_XXS.gguf) | `IQ2_XXS` | 0.49 GB | Experimental. |
+| [manaca-1b-base-Q2_0.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q2_0.gguf) | `Q2_0` | 0.56 GB | Extreme, group-64. Included for completeness. |
+| [manaca-1b-base-IQ1_M.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ1_M.gguf) | `IQ1_M` | 0.44 GB | Extreme. Expect substantial degradation. |
+| [manaca-1b-base-IQ1_S.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-IQ1_S.gguf) | `IQ1_S` | 0.41 GB | Extreme. Expect substantial degradation. |
+| [manaca-1b-base-Q1_0.gguf](https://huggingface.co/NANI-Nithin/manaca-1b-base-GGUF/blob/main/manaca-1b-base-Q1_0.gguf) | `Q1_0` | 0.31 GB | Extreme. Included for completeness. |
+## Which one should I download?
+Pick the largest file that leaves a couple of gigabytes of headroom on the device you will run it on — the model has to fit in RAM (or VRAM, if you are offloading) alongside the KV cache and the OS.
+- Plenty of memory: **Q6_K** or **Q8_0**.
+- The usual choice: **Q4_K_M**.
+- Tight on memory: **IQ4_XS**, then **IQ3_M**, then **IQ2_M**.
+- The `IQ*` files are imatrix-guided and generally beat a `Q*` file of similar size, at the cost of slightly slower inference on some hardware.
+## Quantization details
+- Importance matrix computed with `llama-imatrix` over 500 rows of [Salesforce/wikitext](https://huggingface.co/datasets/Salesforce/wikitext) (`wikitext-2-raw-v1`).
+- The matrix was computed on the **BF16** weights.
+- K-quants below 6 bit and the whole `IQ` set are imatrix-guided. `Q4_0`/`Q4_1`/`Q5_0`/`Q5_1` are legacy round-to-nearest and ignore it; `Q6_K`/`Q8_0` are near-lossless and do not need it.
+- All files are cut from the same BF16 GGUF, so differences between them are quantization only.
+## Usage
+```bash
+llama-cli -hf NANI-Nithin/manaca-1b-base-GGUF:Q4_K_M -p "Hello"
+```
+Or download one file and point at it directly:
+```bash
+huggingface-cli download NANI-Nithin/manaca-1b-base-GGUF manaca-1b-base-Q4_K_M.gguf --local-dir .
+llama-cli -m manaca-1b-base-Q4_K_M.gguf -p "Hello"
+```
+---
+Quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp) by AgentQuantix on 2026-09-05. Licensing follows the base model.