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
- llama.cpp
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
Upload README.md with huggingface_hub
Browse files
README.md
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- gguf
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- llama.cpp
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- quantized
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- imatrix
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---
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# manaca-1b-base GGUF
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| [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. |
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| [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. |
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| [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. |
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| [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;
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| [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. |
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| [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. |
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| [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,
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| [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. |
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| [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. |
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| [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. |
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- Plenty of memory: **Q6_K** or **Q8_0**.
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- The usual choice: **Q4_K_M**.
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- Tight on memory: **IQ4_XS**, then **IQ3_M**, then **IQ2_M**.
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- The `IQ*` files
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## Quantization details
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- Importance matrix computed with `llama-imatrix` over 500 rows of [Salesforce/wikitext](https://huggingface.co/datasets/Salesforce/wikitext) (`wikitext-2-raw-v1`).
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- The matrix was computed on the **BF16** weights.
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- 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.
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- All files are cut from the same BF16 GGUF, so differences between them are quantization only.
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## Usage
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Quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp) by AgentQuantix on 2026-09-
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- gguf
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- llama.cpp
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- quantized
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---
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# manaca-1b-base GGUF
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| [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. |
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| [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. |
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| [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. |
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| [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; the last coherent step down. |
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| [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. |
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| [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. |
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| [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, at a further quality cost. |
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| [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. |
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| [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. |
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| [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. |
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- Plenty of memory: **Q6_K** or **Q8_0**.
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- The usual choice: **Q4_K_M**.
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- Tight on memory: **IQ4_XS**, then **IQ3_M**, then **IQ2_M**.
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- The `IQ*` files generally beat a `Q*` file of similar size, at the cost of slightly slower inference on some hardware.
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## Usage
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Quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp) by AgentQuantix on 2026-09-07. Licensing follows the base model.
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