Instructions to use kelvinlamptey/Solid_LFM2.5-2.6B-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 kelvinlamptey/Solid_LFM2.5-2.6B-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 kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kelvinlamptey/Solid_LFM2.5-2.6B-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 kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kelvinlamptey/Solid_LFM2.5-2.6B-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 kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kelvinlamptey/Solid_LFM2.5-2.6B-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 kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kelvinlamptey/Solid_LFM2.5-2.6B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kelvinlamptey/Solid_LFM2.5-2.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Ollama
How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with Ollama:
```
ollama run hf.co/kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M
```

Lemonade

How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Solid_LFM2.5-2.6B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kelvinlamptey/Solid_LFM2.5-2.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kelvinlamptey/Solid_LFM2.5-2.6B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Kelvin

bartowski commited on 11 days ago

Commit

3933b90

0 Parent(s):

Duplicate from bartowski/LiquidAI_LFM2.5-2.6B-GGUF

Browse files

Co-authored-by: Bartowski <bartowski@users.noreply.huggingface.co>

Files changed (27) hide show

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	@@ -0,0 +1,175 @@

+---
+quantized_by: bartowski
+pipeline_tag: text-generation
+license_link: LICENSE
+language:
+- ar
+- zh
+- en
+- fr
+- de
+- hi
+- id
+- it
+- ja
+- ko
+- pl
+- pt
+- ru
+- es
+- th
+- vi
+base_model_relation: quantized
+base_model: LiquidAI/LFM2.5-2.6B
+license_name: lfm1.0
+tags:
+- liquid
+- lfm2.5
+- edge
+license: other
+---
+## Llamacpp imatrix Quantizations of LFM2.5-2.6B by LiquidAI
+Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10262">b10262</a> for quantization.
+Original model: https://huggingface.co/LiquidAI/LFM2.5-2.6B
+**Model details:**
+- Parameter count: 3B
+- Input support: text
+- MTP: no
+- imatrix: yes - [details](#imatrix)
+[How to run](#how-to-run)
+## Prompt format
+```
+<|startoftext|><|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+<think>
+```
+**Don't know which to choose?** Grab [Q4_K_M](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf) (1.68GB) - usually a good mix of size and performance. Download instructions available [here](#downloading-using-the-hugging-face-cli)
+## Available files:
+| Filename | Quant type | File Size | Split | Description |
+| -------- | ---------- | --------- | ----- | ----------- |
+| [LiquidAI_LFM2.5-2.6B-bf16.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-bf16.gguf) | bf16 | 5.40GB | false | Full BF16 weights. |
+| [LiquidAI_LFM2.5-2.6B-Q8_0.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q8_0.gguf) | Q8_0 | 2.87GB | false | Extremely high quality, generally unneeded but max available quant. |
+| [LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q6_K_L.gguf) | Q6_K_L | 2.30GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q6_K.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q6_K.gguf) | Q6_K | 2.24GB | false | Very high quality, near perfect, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_L.gguf) | Q5_K_L | 2.01GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_M.gguf) | Q5_K_M | 1.95GB | false | High quality, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q5_K_S.gguf) | Q5_K_S | 1.90GB | false | High quality, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q4_1.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_1.gguf) | Q4_1 | 1.75GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
+| [LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_L.gguf) | Q4_K_L | 1.75GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf) | Q4_K_M | 1.68GB | false | Good quality, default size for most use cases, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_K_S.gguf) | Q4_K_S | 1.61GB | false | Slightly lower quality with more space savings, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q4_0.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q4_0.gguf) | Q4_0 | 1.60GB | false | Legacy format, kept for compatibility with older tools. |
+| [LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ4_NL.gguf) | IQ4_NL | 1.60GB | false | Similar to IQ4_XS, but slightly larger. |
+| [LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ4_XS.gguf) | IQ4_XS | 1.52GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
+| [LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_XL.gguf) | Q3_K_XL | 1.52GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
+| [LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_L.gguf) | Q3_K_L | 1.45GB | false | Lower quality but usable, good for low RAM availability. |
+| [LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_M.gguf) | Q3_K_M | 1.37GB | false | Low quality. |
+| [LiquidAI_LFM2.5-2.6B-IQ3_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_M.gguf) | IQ3_M | 1.29GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
+| [LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q3_K_S.gguf) | Q3_K_S | 1.28GB | false | Low quality, not recommended. |
+| [LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_XS.gguf) | IQ3_XS | 1.23GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
+| [LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q2_K_L.gguf) | Q2_K_L | 1.17GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ3_XXS.gguf) | IQ3_XXS | 1.13GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
+| [LiquidAI_LFM2.5-2.6B-Q2_K.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-Q2_K.gguf) | Q2_K | 1.10GB | false | Very low quality but surprisingly usable. |
+| [LiquidAI_LFM2.5-2.6B-IQ2_M.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-IQ2_M.gguf) | IQ2_M | 1.02GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
+Download a specific file:
+```
+hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./
+```
+## Downloading using the Hugging Face CLI
+<details>
+  <summary>Click to view download instructions</summary>
+First, make sure you have the Hugging Face CLI installed:
+```
+pip install -U "huggingface_hub[cli]"
+```
+Download a specific file:
+```
+hf download bartowski/LiquidAI_LFM2.5-2.6B-GGUF --include "LiquidAI_LFM2.5-2.6B-Q4_K_M.gguf" --local-dir ./
+```
+</details>
+## How to run
+These quants run with [llama.cpp](https://github.com/ggml-org/llama.cpp) - installable in one line via [llama.app](https://llama.app/):
+```
+curl -LsSf https://llama.app/install.sh | sh
+llama-server -hf bartowski/LiquidAI_LFM2.5-2.6B-GGUF:Q4_K_M
+```
+llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.
+These quants were made with llama.cpp release b10262 - if this model's architecture is newly supported, you'll need that release or newer to run them.
+They also work in: [LM Studio](https://lmstudio.ai/) · [koboldcpp](https://github.com/LostRuins/koboldcpp) · [ramalama](https://github.com/containers/ramalama) · [Jan AI](https://www.jan.ai/) · [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui) · [LoLLMs](https://github.com/ParisNeo/lollms) · [Atomic Chat](https://atomic.chat/)
+## imatrix
+All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d). The imatrix is available here: [LiquidAI_LFM2.5-2.6B-imatrix.gguf](https://huggingface.co/bartowski/LiquidAI_LFM2.5-2.6B-GGUF/blob/main/LiquidAI_LFM2.5-2.6B-imatrix.gguf).
+## Embed/output weights
+Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
+## ARM/AVX information
+llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in [this PR](https://github.com/ggml-org/llama.cpp/pull/9921). This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.
+## Which file should I choose?
+<details>
+  <summary>Click here for details</summary>
+An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
+The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
+If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
+If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
+Hugging Face can also do this math for you: add your hardware in your [Local Apps settings](https://huggingface.co/settings/local-apps) and the model page will show which files fit.
+Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
+If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
+If you want to get more into the weeds, you can check out this extremely useful feature chart:
+[llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix)
+But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
+These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
+</details>
+## Credits
+Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
+Thank you ZeroWw for the inspiration to experiment with embed/output.
+Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski