Instructions to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic 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 saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic: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 saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic: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 saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Ollama
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Ollama:
```
ollama run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
```

Unsloth Studio

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic to start chatting

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Run and chat with the model

lemonade run user.lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic: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 saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic: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 "saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic: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"

saidutta69 commited on 2 days ago

Commit

5dfa089

verified ·

1 Parent(s): a1fb640

Update model card with full 15-file quant table

Browse files

Files changed (1) hide show

README.md +20 -8

README.md CHANGED Viewed

@@ -37,18 +37,30 @@ Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model'
 ## Files
-### Safetensors (BF16)
-The full-precision merged weights are in the safetensors files (see the repo file listing for exact sizes).
 ### GGUF quantizations
-GGUF quantizations are published for this model (Q4_K_M, Q5_K_M, Q6_K, Q8_0). Pull a specific quant with `llama.cpp` / `ollama`.
 | File | Format | Size |
 |---|---|---|
-| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_K_M.gguf` | GGUF Q4_K_M | ~1.5 GB |
-| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q5_K_M.gguf` | GGUF Q5_K_M | ~1.7 GB |
-| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q6_K.gguf` | GGUF Q6_K | ~1.9 GB |
-| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q8_0.gguf` | GGUF Q8_0 | ~2.5 GB |
 ## Quickstart

 ## Files
 ### GGUF quantizations
+Full quantization set (14 quants + F16) produced with [llama.cpp](https://github.com/ggml-org/llama.cpp).
 | File | Format | Size |
 |---|---|---|
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-F16.gguf` | GGUF F16 | 5.03 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q2_K.gguf` | GGUF Q2_K | 1.02 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-IQ3_S.gguf` | GGUF IQ3_S | 1.18 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q3_K_S.gguf` | GGUF Q3_K_S | 1.18 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q3_K_M.gguf` | GGUF Q3_K_M | 1.27 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q3_K_L.gguf` | GGUF Q3_K_L | 1.35 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-IQ4_XS.gguf` | GGUF IQ4_XS | 1.42 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_K_S.gguf` | GGUF Q4_K_S | 1.49 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_0.gguf` | GGUF Q4_0 | 1.48 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_1.gguf` | GGUF Q4_1 | 1.63 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q4_K_M.gguf` | GGUF Q4_K_M | 1.56 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q5_K_S.gguf` | GGUF Q5_K_S | 1.77 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q5_K_M.gguf` | GGUF Q5_K_M | 1.81 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q6_K.gguf` | GGUF Q6_K | 2.07 GB |
+| `lfm2.5-2.6b-fable5-coding-agent-heretic-Q8_0.gguf` | GGUF Q8_0 | 2.68 GB |
+LFM2 hybrid conv+attention architecture — loads natively in llama.cpp (arch `lfm2`).
+Run `llama serve -hf saidutta69/lfm2.5-2.6b-fable5-coding-agent-heretic` to pull the default quant.
 ## Quickstart