Instructions to use blackmount8/open-llama-7b-open-instruct-ct2-float16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blackmount8/open-llama-7b-open-instruct-ct2-float16 with Transformers:

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

pipe = pipeline("text-generation", model="blackmount8/open-llama-7b-open-instruct-ct2-float16")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("blackmount8/open-llama-7b-open-instruct-ct2-float16", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use blackmount8/open-llama-7b-open-instruct-ct2-float16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blackmount8/open-llama-7b-open-instruct-ct2-float16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blackmount8/open-llama-7b-open-instruct-ct2-float16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/blackmount8/open-llama-7b-open-instruct-ct2-float16

SGLang

How to use blackmount8/open-llama-7b-open-instruct-ct2-float16 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 "blackmount8/open-llama-7b-open-instruct-ct2-float16" \
    --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": "blackmount8/open-llama-7b-open-instruct-ct2-float16",
		"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 "blackmount8/open-llama-7b-open-instruct-ct2-float16" \
        --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": "blackmount8/open-llama-7b-open-instruct-ct2-float16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use blackmount8/open-llama-7b-open-instruct-ct2-float16 with Docker Model Runner:
```
docker model run hf.co/blackmount8/open-llama-7b-open-instruct-ct2-float16
```

blackmount8/open-llama-7B-open-instruct-ct2-float16

Float16 version of VMware/open-llama-7b-open-instruct, quantized using CTranslate2.

VMware/open-llama-7B-open-instruct

Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for COMMERCIAL USE .  

 NOTE  : The model was trained using the Alpaca prompt template  NOTE  : Fast tokenizer results in incorrect encoding, set the use_fast = False parameter, when instantiating the tokenizer

License

Commercially Viable 
Instruction dataset, VMware/open-instruct-v1-oasst-dolly-hhrlhf is under cc-by-sa-3.0
Language Model, (openlm-research/open_llama_7b) is under apache-2.0

Nomenclature

Model : Open-llama
Model Size: 7B parameters
Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf)

Use in CTranslate2

import ctranslate2
from transformers import AutoTokenizer

model_name = "blackmount8/open-llama-7b-open-instruct-ct2-float16"

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left")
model = ctranslate2.Generator(model_name, device="auto", compute_type="float16")

input_text = ["What is the meaning of stonehenge?", "Hello mate!"]

input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids
input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids]

outputs = model.generate_batch(input_tokens, max_length=128)

output_tokens = [
    ele.sequences_ids[0] for ele in outputs
]

output = tokenizer.batch_decode(output_tokens)

print(output)

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blackmount8
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open-llama-7b-open-instruct-ct2-float16

blackmount8/open-llama-7B-open-instruct-ct2-float16

VMware/open-llama-7B-open-instruct

License

Nomenclature

Use in CTranslate2

Dataset used to train blackmount8/open-llama-7b-open-instruct-ct2-float16