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)

# 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")
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=40)
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Code-290k-6.7B-Instruct

This model is trained on DeepSeek-Coder-6.7B-Instruct. I have used my existing dataset 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

Extremely thankful to 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

Bayes Theorem - Python

Fermat's little theorem

The Arrhenius equation using R

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

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

Downloads last month: 69

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ajibawa-2023/Code-290k-6.7B-Instruct

Quantizations

2 models

Dataset used to train ajibawa-2023/Code-290k-6.7B-Instruct

Evaluation results

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

34.900
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

51.990
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

34.890
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

41.950
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

52.640
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

3.490