Instructions to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF",
	filename="open_llama_3b_code_instruct_0.1-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

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 tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

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 tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

SGLang

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with Ollama:
```
ollama run hf.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF 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 tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF 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 tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF to start chatting

Docker Model Runner
How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K
```

Lemonade

How to use tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF:Q2_K

Run and chat with the model

lemonade run user.mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF-Q2_K

List all available models

lemonade list

mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF / README.md

morriszms

Update README.md

648653a verified 10 months ago

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metadata

language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - transformers
  - TensorBlock
  - GGUF
datasets:
  - mwitiderrick/AlpacaCode
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
inference: true
model_type: llama
prompt_template: |
  ### Instruction:\n
  {prompt}
  ### Response:
created_by: mwitiderrick
pipeline_tag: text-generation
model-index:
  - name: mwitiderrick/open_llama_3b_instruct_v_0.2
    results:
      - task:
          type: text-generation
        dataset:
          name: hellaswag
          type: hellaswag
        metrics:
          - type: hellaswag (0-Shot)
            value: 0.6581
            name: hellaswag(0-Shot)
      - task:
          type: text-generation
        dataset:
          name: winogrande
          type: winogrande
        metrics:
          - type: winogrande (0-Shot)
            value: 0.6267
            name: winogrande(0-Shot)
      - task:
          type: text-generation
        dataset:
          name: arc_challenge
          type: arc_challenge
        metrics:
          - type: arc_challenge (0-Shot)
            value: 0.3712
            name: arc_challenge(0-Shot)
        source:
          url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
          name: open_llama_3b_instruct_v_0.2 model card
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 41.21
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 66.96
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 27.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 35.01
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 65.43
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 1.9
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard

mwitiderrick/open_llama_3b_code_instruct_0.1 - GGUF

This repo contains GGUF format model files for mwitiderrick/open_llama_3b_code_instruct_0.1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Prompt template

Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.

Model file specification

Filename	Quant type	File Size	Description
open_llama_3b_code_instruct_0.1-Q2_K.gguf	Q2_K	1.980 GB	smallest, significant quality loss - not recommended for most purposes
open_llama_3b_code_instruct_0.1-Q3_K_S.gguf	Q3_K_S	1.980 GB	very small, high quality loss
open_llama_3b_code_instruct_0.1-Q3_K_M.gguf	Q3_K_M	2.139 GB	very small, high quality loss
open_llama_3b_code_instruct_0.1-Q3_K_L.gguf	Q3_K_L	2.215 GB	small, substantial quality loss
open_llama_3b_code_instruct_0.1-Q4_0.gguf	Q4_0	1.980 GB	legacy; small, very high quality loss - prefer using Q3_K_M
open_llama_3b_code_instruct_0.1-Q4_K_S.gguf	Q4_K_S	2.403 GB	small, greater quality loss
open_llama_3b_code_instruct_0.1-Q4_K_M.gguf	Q4_K_M	2.580 GB	medium, balanced quality - recommended
open_llama_3b_code_instruct_0.1-Q5_0.gguf	Q5_0	2.395 GB	legacy; medium, balanced quality - prefer using Q4_K_M
open_llama_3b_code_instruct_0.1-Q5_K_S.gguf	Q5_K_S	2.603 GB	large, low quality loss - recommended
open_llama_3b_code_instruct_0.1-Q5_K_M.gguf	Q5_K_M	2.757 GB	large, very low quality loss - recommended
open_llama_3b_code_instruct_0.1-Q6_K.gguf	Q6_K	3.642 GB	very large, extremely low quality loss
open_llama_3b_code_instruct_0.1-Q8_0.gguf	Q8_0	3.642 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --include "open_llama_3b_code_instruct_0.1-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'