Instructions to use PollardWeights/Ling-3.0-tiny-Pollard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use PollardWeights/Ling-3.0-tiny-Pollard 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 PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

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 PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
# Run inference directly in the terminal:
./llama-cli -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

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 PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

Use Docker

docker model run hf.co/PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

LM Studio
Jan

vLLM

How to use PollardWeights/Ling-3.0-tiny-Pollard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PollardWeights/Ling-3.0-tiny-Pollard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PollardWeights/Ling-3.0-tiny-Pollard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

Ollama
How to use PollardWeights/Ling-3.0-tiny-Pollard with Ollama:
```
ollama run hf.co/PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
```

Unsloth Studio

How to use PollardWeights/Ling-3.0-tiny-Pollard 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 PollardWeights/Ling-3.0-tiny-Pollard 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 PollardWeights/Ling-3.0-tiny-Pollard to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PollardWeights/Ling-3.0-tiny-Pollard to start chatting

How to use PollardWeights/Ling-3.0-tiny-Pollard with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

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": "PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PollardWeights/Ling-3.0-tiny-Pollard with Docker Model Runner:
```
docker model run hf.co/PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
```

Lemonade

How to use PollardWeights/Ling-3.0-tiny-Pollard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

Run and chat with the model

lemonade run user.Ling-3.0-tiny-Pollard-IQ3_S

List all available models

lemonade list

Hermes Agent

How to use PollardWeights/Ling-3.0-tiny-Pollard with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

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 PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PollardWeights/Ling-3.0-tiny-Pollard with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S

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 "PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S" \
  --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"

westwaters commited on 2 days ago

Commit

74c35dc

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1 Parent(s): 9c06103

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README.md +11 -11

README.md CHANGED Viewed

@@ -77,13 +77,13 @@ detailed thinking on<|role_end|><role>HUMAN</role>{prompt}<|role_end|><role>ASSI
 Pick the rung for your machine — each is the **same weights**, sized to a different
 RAM budget by the measured allocation:
-- **~8 GB RAM / VRAM** → **`fit4GB`** (3.83 GB). The value pick: full model with room
   for context, and it beats same-size uniform IQ3 (table above). **Recommended.**
-- **~9 GB** → **`fit5GB`** (4.64 GB). More fidelity — the sensitive layers move up to
   `iq4_xs`.
-- **~11 GB** → **`fit6GB`** (6.26 GB). Near-lossless; as close to the full model as a
   quant gets.
-- Want it even smaller than fit4GB? Pollard *loses* to uniform at the extreme IQ2 floor
   for this model (the weights are too crushed for reallocation to help), so we don't
   ship one — *measure first, no claim before a number.*
@@ -91,9 +91,9 @@ RAM budget by the measured allocation:
 | Filename | Type | Size | Description |
 |---|---|---|---|
-| [Ling-3.0-tiny-Pollard-fit4GB.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-fit4GB.gguf) | IQ3 measured mix (IQ2_S→IQ4_XS, q6_K embed/attn) | 3.83 GB | Fits an ~8 GB box. Beats same-size uniform IQ3 (table above). **Recommended.** |
-| [Ling-3.0-tiny-Pollard-fit5GB.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-fit5GB.gguf) | IQ4_XS measured mix (q6_K/q5_K attn, q6_K embed) | 4.64 GB | Fits an ~9 GB box. Higher fidelity — sensitive layers pushed to iq4_xs. |
-| [Ling-3.0-tiny-Pollard-fit6GB.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-fit6GB.gguf) | Q5/Q6 measured mix (18L q6_K, 6L q5_K) | 6.26 GB | Fits an ~11 GB box. Near-lossless — maximum quality. |
 | [Ling-3.0-tiny-Pollard.imatrix](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard.imatrix) | importance matrix | 44 MB | The imatrix used, for anyone re-quantizing. |
 | [Ling-3.0-tiny-Pollard-calibration.txt](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-calibration.txt) | calibration corpus | ~1 MB | The exact corpus the imatrix was computed on. |
 | [Ling-3.0-tiny-Pollard.tensor-types.txt](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard.tensor-types.txt) | allocation map | 3 KB | The measured per-tensor bit assignment. |
@@ -103,7 +103,7 @@ RAM budget by the measured allocation:
 ```bash
 pip install -U "huggingface_hub[cli]"
 hf download PollardWeights/Ling-3.0-tiny-Pollard \
-  --include "Ling-3.0-tiny-Pollard-fit4GB.gguf" --local-dir ./
 ```
 ## How to run
@@ -112,14 +112,14 @@ These are standard GGUF and run with **llama.cpp** — one-line install:
 ```bash
 curl -LsSf https://llama.app/install.sh | sh
-llama-server -hf PollardWeights/Ling-3.0-tiny-Pollard:fit4GB
 ```
 or with a local file:
 ```bash
-llama-cli    -m Ling-3.0-tiny-Pollard-fit4GB.gguf -ngl 99 -p "Explain MoE routing simply."
-llama-server -m Ling-3.0-tiny-Pollard-fit4GB.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080
 ```
 They also work in anything built on llama.cpp — **LM Studio, koboldcpp, ramalama,

 Pick the rung for your machine — each is the **same weights**, sized to a different
 RAM budget by the measured allocation:
+- **~8 GB RAM / VRAM** → **`IQ3_S`** (3.83 GB). The value pick: full model with room
   for context, and it beats same-size uniform IQ3 (table above). **Recommended.**
+- **~9 GB** → **`IQ4_XS`** (4.64 GB). More fidelity — the sensitive layers move up to
   `iq4_xs`.
+- **~11 GB** → **`Q6_K`** (6.26 GB). Near-lossless; as close to the full model as a
   quant gets.
+- Want it even smaller than IQ3_S? Pollard *loses* to uniform at the extreme IQ2 floor
   for this model (the weights are too crushed for reallocation to help), so we don't
   ship one — *measure first, no claim before a number.*
 | Filename | Type | Size | Description |
 |---|---|---|---|
+| [Ling-3.0-tiny-Pollard-IQ3_S.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-IQ3_S.gguf) | IQ3 measured mix (IQ2_S→IQ4_XS, q6_K embed/attn) | 3.83 GB | Fits an ~8 GB box. Beats same-size uniform IQ3 (table above). **Recommended.** |
+| [Ling-3.0-tiny-Pollard-IQ4_XS.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-IQ4_XS.gguf) | IQ4_XS measured mix (q6_K/q5_K attn, q6_K embed) | 4.64 GB | Fits an ~9 GB box. Higher fidelity — sensitive layers pushed to iq4_xs. |
+| [Ling-3.0-tiny-Pollard-Q6_K.gguf](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-Q6_K.gguf) | Q5/Q6 measured mix (18L q6_K, 6L q5_K) | 6.26 GB | Fits an ~11 GB box. Near-lossless — maximum quality. |
 | [Ling-3.0-tiny-Pollard.imatrix](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard.imatrix) | importance matrix | 44 MB | The imatrix used, for anyone re-quantizing. |
 | [Ling-3.0-tiny-Pollard-calibration.txt](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard-calibration.txt) | calibration corpus | ~1 MB | The exact corpus the imatrix was computed on. |
 | [Ling-3.0-tiny-Pollard.tensor-types.txt](https://huggingface.co/PollardWeights/Ling-3.0-tiny-Pollard/blob/main/Ling-3.0-tiny-Pollard.tensor-types.txt) | allocation map | 3 KB | The measured per-tensor bit assignment. |
 ```bash
 pip install -U "huggingface_hub[cli]"
 hf download PollardWeights/Ling-3.0-tiny-Pollard \
+  --include "Ling-3.0-tiny-Pollard-IQ3_S.gguf" --local-dir ./
 ```
 ## How to run
 ```bash
 curl -LsSf https://llama.app/install.sh | sh
+llama-server -hf PollardWeights/Ling-3.0-tiny-Pollard:IQ3_S
 ```
 or with a local file:
 ```bash
+llama-cli    -m Ling-3.0-tiny-Pollard-IQ3_S.gguf -ngl 99 -p "Explain MoE routing simply."
+llama-server -m Ling-3.0-tiny-Pollard-IQ3_S.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080
 ```
 They also work in anything built on llama.cpp — **LM Studio, koboldcpp, ramalama,