Instructions to use raimondskrauklis/gpt-neo-1.3b-code-conversation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raimondskrauklis/gpt-neo-1.3b-code-conversation with Transformers:

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

pipe = pipeline("text-generation", model="raimondskrauklis/gpt-neo-1.3b-code-conversation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raimondskrauklis/gpt-neo-1.3b-code-conversation")
model = AutoModelForCausalLM.from_pretrained("raimondskrauklis/gpt-neo-1.3b-code-conversation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raimondskrauklis/gpt-neo-1.3b-code-conversation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raimondskrauklis/gpt-neo-1.3b-code-conversation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raimondskrauklis/gpt-neo-1.3b-code-conversation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raimondskrauklis/gpt-neo-1.3b-code-conversation

SGLang

How to use raimondskrauklis/gpt-neo-1.3b-code-conversation 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 "raimondskrauklis/gpt-neo-1.3b-code-conversation" \
    --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": "raimondskrauklis/gpt-neo-1.3b-code-conversation",
		"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 "raimondskrauklis/gpt-neo-1.3b-code-conversation" \
        --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": "raimondskrauklis/gpt-neo-1.3b-code-conversation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raimondskrauklis/gpt-neo-1.3b-code-conversation with Docker Model Runner:
```
docker model run hf.co/raimondskrauklis/gpt-neo-1.3b-code-conversation
```

gpt-neo-1.3b-code-conversation / README.md

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- conversational-ai
	- code-generation
	- python
	- gpt-neo
	- instruction-following
	- codesearchnet
	base_model: EleutherAI/gpt-neo-1.3B
	datasets:
	- OpenAssistant/oasst1
	- code_search_net
	model-index:
	- name: gpt-neo-1.3b-code-conversation
	results:
	- task:
	type: text-generation
	dataset:
	type: code_search_net
	name: CodeSearchNet Python
	metrics:
	- type: loss
	value: 0.4554
	name: Training Loss
	---

	# GPT-Neo 1.3B Enhanced for Code and Conversation

	A fine-tuned version of GPT-Neo 1.3B optimized for both conversational AI and Python code generation. This model combines instruction-following capabilities with comprehensive Python programming knowledge through a multi-layer fine-tuning approach.

	## Model Description

	Base Model: EleutherAI/gpt-neo-1.3B
	Fine-tuning Approach: Multi-layer sequential training
	Specializations: Conversation + Python Code Generation

	### Training Layers:
	1. Conversational Foundation: Fine-tuned on high-quality dialogue data for instruction-following
	2. Code Specialization: Enhanced with 362,059 Python code examples from CodeSearchNet dataset
	3. Integration: Maintains conversational abilities while adding strong coding capabilities

	## Training Details

	- Architecture: GPT-Neo 1.3B (transformer-based autoregressive language model)
	- Training Infrastructure: European HPC systems with AMD GPU acceleration
	- Distributed Training: Multi-GPU setup with gradient accumulation
	- Final Training Loss: 0.4554 (excellent convergence)
	- CodeSearchNet Dataset: 362,059 high-quality Python code-documentation pairs
	- Training Duration: ~6 hours on 8x AMD MI250X GPUs
	- Optimization: AdamW optimizer with cosine annealing schedule

	## Capabilities

	### Code Generation
	- Python Functions: Complete implementations with proper documentation
	- Algorithm Development: Data structures, algorithms, and problem-solving
	- Code Explanation: Clear explanations of functionality and logic
	- Documentation: Automatic docstring and comment generation

	### Conversational AI
	- Instruction Following: Responds appropriately to coding requests
	- Technical Explanations: Breaks down complex programming concepts
	- Problem Solving: Helps debug and optimize code solutions
	- Educational Content: Teaches programming concepts step-by-step

	## Usage Examples

	### Python Code Generation
	```python
	from transformers import GPTNeoForCausalLM, GPT2Tokenizer

	model = GPTNeoForCausalLM.from_pretrained("raimondskrauklis/gpt-neo-1.3b-code-conversation")
	tokenizer = GPT2Tokenizer.from_pretrained("raimondskrauklis/gpt-neo-1.3b-code-conversation")
	tokenizer.pad_token = tokenizer.eos_token

	# Code generation example
	prompt = "Human: Write a Python function that calculates the factorial of a number\nAssistant:"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_length=200, temperature=0.7, do_sample=True)
	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	Code Explanation
	pythonprompt = "Human: Explain how binary search works in Python\nAssistant:"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_length=300, temperature=0.7)
	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	Debugging Assistance
	pythonprompt = "Human: Why does this Python code give a list index error?\ncode: for i in range(len(data)+1): print(data[i])\nAssistant:"
	inputs = tokenizer(prompt, return_tensors="pt")
	outputs = model.generate(**inputs, max_length=250, temperature=0.7)
	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	Training Methodology
	Multi-Layer Fine-tuning Strategy

	Base Selection: Started with EleutherAI's GPT-Neo 1.3B pre-trained model
	Layer 1 - Conversational: Fine-tuned on dialogue data for instruction-following
	Layer 2 - Code Enhancement: Specialized training on CodeSearchNet Python dataset
	Quality Assurance: Rigorous filtering for high-quality code-documentation pairs

	Technical Implementation

	Distributed Training: 8x AMD MI250X GPUs with proper CPU-GPU affinity
	Batch Configuration: Per-device batch size of 4 with gradient accumulation
	Learning Rate: 5e-6 with cosine annealing schedule
	Sequence Length: 512 tokens maximum
	Epochs: 2 epochs over full dataset for optimal convergence

	Performance Metrics

	Training Loss Progression: 0.9556 → 0.4554 (excellent convergence)
	Dataset Coverage: 362,059 Python code examples
	Training Efficiency: ~11,315 batches per epoch
	Model Size: ~5.3GB (2x safetensors files)
	Context Length: 512 tokens

	Limitations

	Language Focus: Primarily trained on Python code (limited other programming languages)
	Code Complexity: Best performance on functions under 100 lines
	Validation Required: Generated code should be tested before production use
	Knowledge Cutoff: Training data reflects pre-2024 coding practices
	Context Window: Limited to 512 tokens for generation

	Ethical Considerations

	Code Review: All generated code should be reviewed for security and correctness
	Bias Awareness: May reflect biases present in training data
	Responsible Use: Not intended for malicious code generation
	Attribution: Based on open-source datasets and models

	Technical Specifications

	Model Type: Causal Language Model (GPT-Neo architecture)
	Parameters: 1.3 billion
	Vocabulary Size: 50,257 tokens
	Hidden Size: 2,048
	Attention Heads: 16
	Layers: 24
	Context Length: 2,048 tokens (training used 512)

	Citation
	bibtex@misc{gpt-neo-code-conversation-2025,
	title={GPT-Neo 1.3B Enhanced for Code and Conversation},
	author={Raimonds Krauklis},
	year={2025},
	howpublished={Hugging Face Model Hub},
	url={https://huggingface.co/raimondskrauklis/gpt-neo-1.3b-code-conversation},
	note={Fine-tuned on European HPC infrastructure using CodeSearchNet dataset}
	}
	Acknowledgments

	Base Model: EleutherAI for GPT-Neo 1.3B
	Dataset: CodeSearchNet by GitHub/Microsoft Research
	Infrastructure: European high-performance computing systems
	Framework: Hugging Face Transformers and PyTorch ecosystem

	Model Card Contact
	For questions about this model, please open an issue in the model repository or contact through Hugging Face.