Instructions to use glaiveai/glaive-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glaiveai/glaive-coder-7b with Transformers:

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

pipe = pipeline("text-generation", model="glaiveai/glaive-coder-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b")
model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use glaiveai/glaive-coder-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/glaiveai/glaive-coder-7b

SGLang

How to use glaiveai/glaive-coder-7b 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 "glaiveai/glaive-coder-7b" \
    --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": "glaiveai/glaive-coder-7b",
		"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 "glaiveai/glaive-coder-7b" \
        --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": "glaiveai/glaive-coder-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use glaiveai/glaive-coder-7b with Docker Model Runner:
```
docker model run hf.co/glaiveai/glaive-coder-7b
```

glaive-coder-7b / README.md

sahil2801

Update README.md

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	---
	license: llama2
	datasets:
	- glaiveai/glaive-code-assistant
	language:
	- en
	tags:
	- code
	---

	# Glaive-coder-7b

	Glaive-coder-7b is a 7B parameter code model trained on a dataset of ~140k programming related problems and solutions generated from Glaive’s synthetic data generation platform.

	The model is fine-tuned on the CodeLlama-7b model.

	## Usage:

	The model is trained to act as a code assistant, and can do both single instruction following and multi-turn conversations.
	It follows the same prompt format as CodeLlama-7b-Instruct-
	```
	<s>[INST]
	<<SYS>>
	{{ system_prompt }}
	<</SYS>>

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

	You can run the model in the following way-

	```python
	from transformers import AutoModelForCausalLM , AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("glaiveai/glaive-coder-7b")
	model = AutoModelForCausalLM.from_pretrained("glaiveai/glaive-coder-7b").half().cuda()

	def fmt_prompt(prompt):
	return f"<s> [INST] {prompt} [/INST]"

	inputs = tokenizer(fmt_prompt(prompt),return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100)

	print(tokenizer.decode(outputs[0],skip_special_tokens=True,clean_up_tokenization_spaces=False))
	```

	## Benchmarks:

	The model achieves a 63.1% pass@1 on HumanEval and a 45.2% pass@1 on MBPP, however it is evident that these benchmarks are not representative of real-world usage of code models so we are launching the [Code Models Arena](https://arena.glaive.ai/) to let users vote on model outputs so we can have a better understanding of user preference on code models and come up with new and better benchmarks. We plan to release the Arena results as soon as we have a sufficient amount of data.

	Join the Glaive [discord](https://discord.gg/fjQ4uf3yWD) for improvement suggestions, bug-reports and collaborating on more open-source projects.