Instructions to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="mwitiderrick/open_llama_3b_glaive_code_v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mwitiderrick/open_llama_3b_glaive_code_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1

SGLang

How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
    --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"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 "mwitiderrick/open_llama_3b_glaive_code_v0.1" \
        --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": "mwitiderrick/open_llama_3b_glaive_code_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mwitiderrick/open_llama_3b_glaive_code_v0.1 with Docker Model Runner:
```
docker model run hf.co/mwitiderrick/open_llama_3b_glaive_code_v0.1
```

OpenLLaMA Glaive: An Open Reproduction of LLaMA

This is an OpenLlama model Code Instruct that has been fine-tuned on 1 epoch of the Glaive Assistsnt dataset.

Prompt Template

<s>[INST] {{ user_msg }} [/INST]

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline

tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_glaive_code_v0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_glaive_v0.1")
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"<s>[INST]{query}[/INST]")
print(output[0]['generated_text'])
"""
<s>[INST]Write a quick sort algorithm in Python[/INST]

Quick sort is a divide and conquer algorithm that sorts an array in-place.
It works by repeatedly dividing the array into two sub-arrays, sorting
them, and then merging them back together.

Here's a Python implementation of the quick sort algorithm:

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]
        right = [x for x in arr if x > pivot]
        return quick_sort(left) + [pivot] + quick_sort
"""

Metrics

Detailed metrics

|  Tasks  |Version|Filter|n-shot| Metric |Value |   |Stderr|
|---------|-------|------|-----:|--------|-----:|---|-----:|
|hellaswag|Yaml   |none  |     0|acc     |0.4974|±  |0.0050|
|         |       |none  |     0|acc_norm|0.6600|±  |0.0047|
|  Groups  |Version|Filter|n-shot|  Metric   | Value  |   |Stderr|
|----------|-------|------|-----:|-----------|-------:|---|-----:|
|truthfulqa|N/A    |none  |     0|bleu_max   | 23.5771|±  |0.5407|
|          |       |none  |     0|bleu_acc   |  0.2754|±  |0.0002|
|          |       |none  |     0|bleu_diff  | -8.1019|±  |0.5137|
|          |       |none  |     0|rouge1_max | 49.5707|±  |0.6501|
|          |       |none  |     0|rouge1_acc |  0.2607|±  |0.0002|
|          |       |none  |     0|rouge1_diff| -9.8962|±  |0.5492|
|          |       |none  |     0|rouge2_max | 33.0399|±  |0.8237|
|          |       |none  |     0|rouge2_acc |  0.2313|±  |0.0002|
|          |       |none  |     0|rouge2_diff|-11.9054|±  |0.7963|
|          |       |none  |     0|rougeL_max | 46.3168|±  |0.6705|
|          |       |none  |     0|rougeL_acc |  0.2521|±  |0.0002|
|          |       |none  |     0|rougeL_diff|-10.1301|±  |0.5669|
|          |       |none  |     0|acc        |  0.3191|±  |0.0405|
|  Tasks   |Version|Filter|n-shot|Metric|Value |   |Stderr|
|----------|-------|------|-----:|------|-----:|---|-----:|
|winogrande|Yaml   |none  |     0|acc   |0.6322|±  |0.0136|
|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge|Yaml   |none  |     0|acc     |0.3234|±  |0.0137|
|             |       |none  |     0|acc_norm|0.3447|±  |0.0139|

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	39.74
AI2 Reasoning Challenge (25-Shot)	40.70
HellaSwag (10-Shot)	67.45
MMLU (5-Shot)	27.74
TruthfulQA (0-shot)	35.86
Winogrande (5-shot)	64.72
GSM8k (5-shot)	1.97

Downloads last month: 86

Safetensors

Model size

3B params

Tensor type

F16

Model tree for mwitiderrick/open_llama_3b_glaive_code_v0.1

Base model

openlm-research/open_llama_3b

Finetuned

mwitiderrick/open_llama_3b_code_instruct_0.1

Finetuned

(1)

this model

Dataset used to train mwitiderrick/open_llama_3b_glaive_code_v0.1

Evaluation results

hellaswag(0-Shot) on hellaswag
self-reported

0.660
winogrande(0-Shot) on winogrande
self-reported

0.632
arc_challenge(0-Shot) on arc_challenge
open_llama_3b_instruct_v_0.2 model card

0.345
normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

40.700
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

67.450
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

27.740
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

35.860
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

64.720