Instructions to use ajibawa-2023/Code-290k-6.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/Code-290k-6.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/Code-290k-6.7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Code-290k-6.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Code-290k-6.7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ajibawa-2023/Code-290k-6.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/Code-290k-6.7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Code-290k-6.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ajibawa-2023/Code-290k-6.7B-Instruct

SGLang

How to use ajibawa-2023/Code-290k-6.7B-Instruct 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 "ajibawa-2023/Code-290k-6.7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Code-290k-6.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ajibawa-2023/Code-290k-6.7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Code-290k-6.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ajibawa-2023/Code-290k-6.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/Code-290k-6.7B-Instruct
```

Code-290k-6.7B-Instruct / README.md

ajibawa-2023

Update README.md

e4a8296 verified over 2 years ago

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	---
	language:
	- en
	license: other
	tags:
	- code
	datasets:
	- ajibawa-2023/Code-290k-ShareGPT
	model-index:
	- name: Code-290k-6.7B-Instruct
	results:
	- 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: 34.9
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	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: 51.99
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	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: 34.89
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	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: 41.95
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	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: 52.64
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	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: 3.49
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-290k-6.7B-Instruct
	name: Open LLM Leaderboard
	---

	Code-290k-6.7B-Instruct

	This model is trained on [DeepSeek-Coder-6.7B-Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct). I have used my existing dataset [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT) for training purpose.
	It is trained on around 290000 set of codes. Along with Python, Java, JavaScript, GO, C++, Rust, Ruby, Sql, MySql, R, Julia, Haskell, etc. code with detailed explanation is used for training purpose.
	This model utilises Alpaca format. Besides code generation it will also give you explanation.

	Training:

	Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 85 hours. DeepSeek-Coder codebase and DeepSpeed was used for training purpose.

	This is a full fine tuned model.

	Links for quantized models are given below.

	Exllama

	Exllama v2:[Link](https://huggingface.co/bartowski/Code-290k-6.7B-Instruct-exl2)

	Extremely thankful to [Bartowski](https://huggingface.co/bartowski) for making Quantized version of the model.


	Example Prompt:

	```
	This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation.

	### Instruction:
	{instruction}

	### Response:
	```
	You can modify above Prompt as per your requirement. I have used Alpaca format.

	I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

	Thank you for your love & support.

	Examples

	1. Bayes Theorem - Python


	![image/png](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/J8uqoT_LYhPW2VpnE1K-8.png)

	2. Fermat's little theorem

	![image/png](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/H0sc9jk7ypv_N5V7LSANl.png)

	3. The Arrhenius equation using R

	![image/png](https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/64aea8ff67511bd3d965697b/BQ8PZhYhoZ9wpVMPXJPnQ.png)


	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-290k-6.7B-Instruct)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|36.64\|
	\|AI2 Reasoning Challenge (25-Shot)\|34.90\|
	\|HellaSwag (10-Shot) \|51.99\|
	\|MMLU (5-Shot) \|34.89\|
	\|TruthfulQA (0-shot) \|41.95\|
	\|Winogrande (5-shot) \|52.64\|
	\|GSM8k (5-shot) \| 3.49\|