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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	---
	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
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>

	[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
	[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)
	[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)
	[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)
	[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)


	## 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](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

	## Our projects
	<table border="1" cellspacing="0" cellpadding="10">
	<tr>
	<th colspan="2" style="font-size: 25px;">Forge</th>
	</tr>
	<tr>
	<th colspan="2">
	<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
	</th>
	</tr>
	<tr>
	<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
	</tr>
	<tr>
	<th colspan="2">
	<a href="https://github.com/TensorBlock/forge" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">🚀 Try it now! 🚀</a>
	</th>
	</tr>

	<tr>
	<th style="font-size: 25px;">Awesome MCP Servers</th>
	<th style="font-size: 25px;">TensorBlock Studio</th>
	</tr>
	<tr>
	<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
	<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
	</tr>
	<tr>
	<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
	<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
	</tr>
	<tr>
	<th>
	<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	<th>
	<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	</tr>
	</table>

	## 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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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](https://huggingface.co/tensorblock/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF/blob/main/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

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	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_Kgguf`), you can try:

	```shell
	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_Kgguf'
	```