Instructions to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
```
Unsloth Desktop

How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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": "RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M

Run and chat with the model

lemonade run user.sequelbox_-_Llama3.1-8B-PlumCode-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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 "RichardErkhov/sequelbox_-_Llama3.1-8B-PlumCode-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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Llama3.1-8B-PlumCode - GGUF

Model creator: https://huggingface.co/sequelbox/
Original model: https://huggingface.co/sequelbox/Llama3.1-8B-PlumCode/

Name	Quant method	Size
Llama3.1-8B-PlumCode.Q2_K.gguf	Q2_K	2.96GB
Llama3.1-8B-PlumCode.Q3_K_S.gguf	Q3_K_S	3.41GB
Llama3.1-8B-PlumCode.Q3_K.gguf	Q3_K	3.12GB
Llama3.1-8B-PlumCode.Q3_K_M.gguf	Q3_K_M	3.26GB
Llama3.1-8B-PlumCode.Q3_K_L.gguf	Q3_K_L	4.03GB
Llama3.1-8B-PlumCode.IQ4_XS.gguf	IQ4_XS	4.18GB
Llama3.1-8B-PlumCode.Q4_0.gguf	Q4_0	4.34GB
Llama3.1-8B-PlumCode.IQ4_NL.gguf	IQ4_NL	4.38GB
Llama3.1-8B-PlumCode.Q4_K_S.gguf	Q4_K_S	4.37GB
Llama3.1-8B-PlumCode.Q4_K.gguf	Q4_K	4.58GB
Llama3.1-8B-PlumCode.Q4_K_M.gguf	Q4_K_M	4.58GB
Llama3.1-8B-PlumCode.Q4_1.gguf	Q4_1	4.78GB
Llama3.1-8B-PlumCode.Q5_0.gguf	Q5_0	5.21GB
Llama3.1-8B-PlumCode.Q5_K_S.gguf	Q5_K_S	5.21GB
Llama3.1-8B-PlumCode.Q5_K.gguf	Q5_K	5.34GB
Llama3.1-8B-PlumCode.Q5_K_M.gguf	Q5_K_M	5.34GB
Llama3.1-8B-PlumCode.Q5_1.gguf	Q5_1	5.65GB
Llama3.1-8B-PlumCode.Q6_K.gguf	Q6_K	6.14GB
Llama3.1-8B-PlumCode.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers tags: - mergekit - merge - shining-valiant - shining-valiant-2 - enigma - plum - plumcode - code - valiant - valiant-labs - llama - llama-3.1 - llama-3.1-instruct - llama-3.1-instruct-8b - llama-3 - llama-3-instruct - llama-3-instruct-8b - 8b - code - code-instruct - python - science - physics - biology - chemistry - compsci - computer-science - engineering - technical - conversational - chat - instruct base_model: - meta-llama/Llama-3.1-8B-Instruct - ValiantLabs/Llama3.1-8B-Enigma - ValiantLabs/Llama3.1-8B-ShiningValiant2 model-index: - name: Llama3.1-8B-PlumCode results: - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-Shot) type: Winogrande args: num_few_shot: 5 metrics: - type: acc value: 73.16 name: acc - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 20.45 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 8.5 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 2.42 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 3.47 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 8.97 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 14.84 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sequelbox/Llama3.1-8B-PlumCode name: Open LLM Leaderboard

PlumCode

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the della merge method using meta-llama/Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: della
dtype: bfloat16
parameters:
  normalize: true
models:
  - model: ValiantLabs/Llama3.1-8B-ShiningValiant2
    parameters:
      density: 0.5
      weight: 0.3
  - model: ValiantLabs/Llama3.1-8B-Enigma
    parameters:
      density: 0.5
      weight: 0.25
base_model: meta-llama/Llama-3.1-8B-Instruct

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	9.77
IFEval (0-Shot)	20.45
BBH (3-Shot)	8.50
MATH Lvl 5 (4-Shot)	2.42
GPQA (0-shot)	3.47
MuSR (0-shot)	8.97
MMLU-PRO (5-shot)	14.84

Downloads last month: 34

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

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4-bit

5-bit

6-bit

8-bit

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