Instructions to use afrideva/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 afrideva/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="afrideva/open_llama_3b_code_instruct_0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("afrideva/open_llama_3b_code_instruct_0.1-GGUF", dtype="auto")

llama-cpp-python

How to use afrideva/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="afrideva/open_llama_3b_code_instruct_0.1-GGUF",
	filename="open_llama_3b_code_instruct_0.1.fp16.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 afrideva/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 afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/open_llama_3b_code_instruct_0.1-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 afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/open_llama_3b_code_instruct_0.1-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 afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/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 "afrideva/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": "afrideva/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/afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M

SGLang

How to use afrideva/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 "afrideva/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": "afrideva/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 "afrideva/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": "afrideva/open_llama_3b_code_instruct_0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use afrideva/open_llama_3b_code_instruct_0.1-GGUF with Ollama:
```
ollama run hf.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/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 afrideva/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 afrideva/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 afrideva/open_llama_3b_code_instruct_0.1-GGUF to start chatting

Docker Model Runner
How to use afrideva/open_llama_3b_code_instruct_0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/open_llama_3b_code_instruct_0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/open_llama_3b_code_instruct_0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.open_llama_3b_code_instruct_0.1-GGUF-Q4_K_M

List all available models

lemonade list

mwitiderrick/open_llama_3b_code_instruct_0.1-GGUF

Quantized GGUF model files for open_llama_3b_code_instruct_0.1 from mwitiderrick

Name	Quant method	Size
open_llama_3b_code_instruct_0.1.fp16.gguf	fp16	6.86 GB
open_llama_3b_code_instruct_0.1.q2_k.gguf	q2_k	2.15 GB
open_llama_3b_code_instruct_0.1.q3_k_m.gguf	q3_k_m	2.27 GB
open_llama_3b_code_instruct_0.1.q4_k_m.gguf	q4_k_m	2.58 GB
open_llama_3b_code_instruct_0.1.q5_k_m.gguf	q5_k_m	2.76 GB
open_llama_3b_code_instruct_0.1.q6_k.gguf	q6_k	3.64 GB
open_llama_3b_code_instruct_0.1.q8_0.gguf	q8_0	3.64 GB

Original Model Card:

OpenLLaMA Code Instruct: An Open Reproduction of LLaMA

This is an OpenLlama model that has been fine-tuned on 1 epoch of the AlpacaCode dataset (122K rows).

Prompt Template

### Instruction:

{query}

### Response:
<Leave new line for model to respond>

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1")
query = "Write a quick sort algorithm in Python"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
print(output[0]['generated_text'])
"""
### Instruction:
write a quick sort algorithm in Python
### Response:
def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    else:
        pivot = arr[len(arr) // 2]
        left = [x for x in arr if x < pivot]
        middle = [x for x in arr if x == pivot]
        right = [x for x in arr if x > pivot]
        return quick_sort(left) + middle + quick_sort(right)

arr = [5,2,4,3,1]
print(quick_sort(arr))
"""
[1, 2, 3, 4, 5]
"""

Metrics

Detailed metrics

|  Tasks   |Version|Filter|n-shot|Metric|Value |   |Stderr|
|----------|-------|------|-----:|------|-----:|---|-----:|
|winogrande|Yaml   |none  |     0|acc   |0.6267|±  |0.0136|
|hellaswag|Yaml   |none  |     0|acc     |0.4962|±  |0.0050|
|         |       |none  |     0|acc_norm|0.6581|±  |0.0047|
|arc_challenge|Yaml   |none  |     0|acc     |0.3481|±  |0.0139|
|             |       |none  |     0|acc_norm|0.3712|±  |0.0141|
|truthfulqa|N/A    |none  |     0|bleu_max   | 24.2580|±  |0.5985|
|          |       |none  |     0|bleu_acc   |  0.2876|±  |0.0003|
|          |       |none  |     0|bleu_diff  | -8.3685|±  |0.6065|
|          |       |none  |     0|rouge1_max | 49.3907|±  |0.7350|
|          |       |none  |     0|rouge1_acc |  0.2558|±  |0.0002|
|          |       |none  |     0|rouge1_diff|-10.6617|±  |0.6450|
|          |       |none  |     0|rouge2_max | 32.4189|±  |0.9587|
|          |       |none  |     0|rouge2_acc |  0.2142|±  |0.0002|
|          |       |none  |     0|rouge2_diff|-12.9903|±  |0.9539|
|          |       |none  |     0|rougeL_max | 46.2337|±  |0.7493|
|          |       |none  |     0|rougeL_acc |  0.2424|±  |0.0002|
|          |       |none  |     0|rougeL_diff|-11.0285|±  |0.6576|
|          |       |none  |     0|acc        |  0.3072|±  |0.0405|

Downloads last month: 82

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/open_llama_3b_code_instruct_0.1-GGUF

Base model

openlm-research/open_llama_3b

Finetuned

mwitiderrick/open_llama_3b_code_instruct_0.1

Quantized

(2)

this model

Dataset used to train afrideva/open_llama_3b_code_instruct_0.1-GGUF

Evaluation results

hellaswag(0-Shot) on hellaswag
self-reported

0.658
winogrande(0-Shot) on winogrande
self-reported

0.627
arc_challenge(0-Shot) on arc_challenge
open_llama_3b_instruct_v_0.2 model card

0.371