Instructions to use vivek078/AutodevvLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vivek078/AutodevvLM with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vivek078/AutodevvLM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vivek078/AutodevvLM:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf vivek078/AutodevvLM:Q4_K_M

Use Docker

docker model run hf.co/vivek078/AutodevvLM:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use vivek078/AutodevvLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vivek078/AutodevvLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vivek078/AutodevvLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vivek078/AutodevvLM:Q4_K_M

Ollama
How to use vivek078/AutodevvLM with Ollama:
```
ollama run hf.co/vivek078/AutodevvLM:Q4_K_M
```
Unsloth Desktop

How to use vivek078/AutodevvLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vivek078/AutodevvLM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vivek078/AutodevvLM with Docker Model Runner:
```
docker model run hf.co/vivek078/AutodevvLM:Q4_K_M
```

Lemonade

How to use vivek078/AutodevvLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vivek078/AutodevvLM:Q4_K_M

Run and chat with the model

lemonade run user.AutodevvLM-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vivek078/AutodevvLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vivek078/AutodevvLM:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vivek078/AutodevvLM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vivek078/AutodevvLM:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

AutodevvLM / README.md

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets:
  - m-a-p/CodeFeedback-Filtered-Instruction
language:
  - en
pipeline_tag: text-generation
library_name: gguf
tags:
  - code
  - coding-assistant
  - qwen2
  - qlora
  - sft
  - gguf
  - llama.cpp
  - ollama
---
# AutoDevvLM

AutoDevvLM is a small, code-focused language model fine-tuned from [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It was trained with QLoRA on general code-instruction data and is distributed in **GGUF** format for local inference with llama.cpp, Ollama, LM Studio and similar tools.

> **Note:** AutoDevvLM is a fine-tune of Qwen2.5-1.5B-Instruct. It is not an independent model, and it may still identify itself as Qwen unless given a system prompt (see [Usage](#usage)).

## Model Details

| | |
|---|---|
| **Developed by** | `Vivek A` |
| **Base model** | `Qwen/Qwen2.5-1.5B-Instruct` |
| **Architecture** | Qwen2 (`Qwen2ForCausalLM`), ~1.5B parameters |
| **Context length** | 32,768 tokens in the architecture; training used sequences up to 1,024 tokens |
| **Fine-tuning method** | Supervised fine-tuning (SFT) with QLoRA |
| **Language** | English (primary) |
| **License** | Apache 2.0 |
| **Format** | GGUF (quantized) |

## Files

| File | Description | Approx. size |
|---|---|---|
| `AutoDevvLM-Q4_K_M.gguf` | 4-bit quantized, recommended for most local use | ~1 GB |
| `AutoDevvLM-f16.gguf` *(optional)* | Full FP16 precision | ~3 GB |

## Intended Use

AutoDevvLM is intended for lightweight, local coding assistance:

- Writing short functions and scripts
- Explaining code and simple debugging
- Answering general programming questions
- Experimentation and learning about fine-tuning small models

## Out-of-Scope Use

- Safety-critical or production-critical code generation without human review
- Security-sensitive tasks (generated code may contain vulnerabilities)
- Factual, legal, medical or financial advice
- Use as a general-purpose assistant for tasks far outside programming

## Usage

The model uses the Qwen (ChatML) chat format. The template is embedded in the GGUF, so most runtimes apply it automatically.

### llama.cpp

```bash
llama-cli -m AutoDevvLM-Q4_K_M.gguf -cnv \
  -sys "You are AutoDevvLM, a helpful coding assistant."
```

### Ollama

Create a `Modelfile`:

```
FROM ./AutoDevvLM-Q4_K_M.gguf
SYSTEM """You are AutoDevvLM, a coding assistant. Help with writing, debugging and explaining code."""
PARAMETER temperature 0.2
```

Then:

```bash
ollama create autodevvlm -f Modelfile
ollama run autodevvlm
```

### Python (llama-cpp-python)

```python
from llama_cpp import Llama
llm = Llama(model_path="AutoDevvLM-Q4_K_M.gguf", n_ctx=4096)
out = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are AutoDevvLM, a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function that checks whether a string is a valid IPv4 address."},
    ],
    max_tokens=400,
    temperature=0.2,
    top_p=0.95,
)
print(out["choices"][0]["message"]["content"])
```

**Suggested sampling settings:** temperature 0.2 to 0.4, top_p 0.95 for code.
## Training Details
### Training data
[`m-a-p/CodeFeedback-Filtered-Instruction`](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) (Apache 2.0). A random subset of **8,000** examples was used after filtering out examples longer than 1,024 tokens, with a 2% hold-out for evaluation loss. Each example was formatted with the Qwen chat template (user query, assistant answer).
### Training procedure
| Setting | Value |
|---|---|
| Method | QLoRA (4-bit NF4 with double quantization, via `bitsandbytes`) |
| Compute precision | FP16 (BF16 not used; NVIDIA T4 lacks native BF16) |
| LoRA rank / alpha / dropout | 16 / 32 / 0.05 |
| LoRA target modules | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` |
| Epochs | 1 |
| Batch size | 2 per device x 8 gradient accumulation (effective 16) |
| Learning rate | 2e-4, cosine schedule, 3% warmup |
| Optimizer | `paged_adamw_8bit` |
| Max grad norm | 0.3 |
| Max sequence length | 1,024 |
| Gradient checkpointing | Enabled |
| Hardware | Google Colab, 1x NVIDIA Tesla T4 (16 GB) |
| Frameworks | Transformers, PEFT, TRL, bitsandbytes |

### Export

The LoRA adapter was merged into the FP16 base model, converted to GGUF with llama.cpp's `convert_hf_to_gguf.py`, and quantized to Q4_K_M with `llama-quantize`.

## Evaluation

No formal benchmark evaluation has been run for this model yet. Results on HumanEval, MBPP or similar benchmarks have not been measured, and no claim is made that it outperforms the base model. Users are encouraged to compare outputs against `Qwen2.5-1.5B-Instruct` on their own tasks.

## Limitations and Risks

- **Small model:** at 1.5B parameters it can produce incorrect, incomplete or insecure code, and may hallucinate APIs or library functions. Always review and test its output.
- **Light fine-tune:** training used 8,000 examples for a single epoch, so behavior is close to the base model, with modest changes in code style and explanation format.
- **Identity:** the training data contained no identity examples, so the model may state that it is Qwen. Use a system prompt to set the assistant name.
- **Quantization:** Q4_K_M trades some quality for size; use the F16 file if you need maximum fidelity.
- **Inherited biases:** the model inherits the limitations and biases of the base model and the training dataset.
- **Language coverage:** performance is strongest in English and in popular programming languages.

## License

Released under the **Apache 2.0** license, the same as the base model and the training dataset. Please retain the license notice and attribution when redistributing.

## Acknowledgements

- [Qwen Team](https://huggingface.co/Qwen) for the base model, Qwen2.5-1.5B-Instruct
- [m-a-p](https://huggingface.co/m-a-p) for the CodeFeedback-Filtered-Instruction dataset
- [llama.cpp](https://github.com/ggml-org/llama.cpp), [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl) and [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes)

## Citation

```bibtex
@misc{autodevvlm2026,
  title  = {AutoDevvLM: A QLoRA fine-tune of Qwen2.5-1.5B-Instruct for code assistance},
  author = {<your-name-or-org>},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/<your-username>/AutoDevvLM}}
}
```

	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	datasets:
	- m-a-p/CodeFeedback-Filtered-Instruction
	language:
	- en
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- code
	- coding-assistant
	- qwen2
	- qlora
	- sft
	- gguf
	- llama.cpp
	- ollama
	---

	# AutoDevvLM

	AutoDevvLM is a small, code-focused language model fine-tuned from [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It was trained with QLoRA on general code-instruction data and is distributed in GGUF format for local inference with llama.cpp, Ollama, LM Studio and similar tools.

	> Note: AutoDevvLM is a fine-tune of Qwen2.5-1.5B-Instruct. It is not an independent model, and it may still identify itself as Qwen unless given a system prompt (see [Usage](#usage)).

	## Model Details

	\| \| \|
	\|---\|---\|
	\| Developed by \| `Vivek A` \|
	\| Base model \| `Qwen/Qwen2.5-1.5B-Instruct` \|
	\| Architecture \| Qwen2 (`Qwen2ForCausalLM`), ~1.5B parameters \|
	\| Context length \| 32,768 tokens in the architecture; training used sequences up to 1,024 tokens \|
	\| Fine-tuning method \| Supervised fine-tuning (SFT) with QLoRA \|
	\| Language \| English (primary) \|
	\| License \| Apache 2.0 \|
	\| Format \| GGUF (quantized) \|

	## Files

	\| File \| Description \| Approx. size \|
	\|---\|---\|---\|
	\| `AutoDevvLM-Q4_K_M.gguf` \| 4-bit quantized, recommended for most local use \| ~1 GB \|
	\| `AutoDevvLM-f16.gguf` (optional) \| Full FP16 precision \| ~3 GB \|

	## Intended Use

	AutoDevvLM is intended for lightweight, local coding assistance:

	- Writing short functions and scripts
	- Explaining code and simple debugging
	- Answering general programming questions
	- Experimentation and learning about fine-tuning small models

	## Out-of-Scope Use

	- Safety-critical or production-critical code generation without human review
	- Security-sensitive tasks (generated code may contain vulnerabilities)
	- Factual, legal, medical or financial advice
	- Use as a general-purpose assistant for tasks far outside programming

	## Usage

	The model uses the Qwen (ChatML) chat format. The template is embedded in the GGUF, so most runtimes apply it automatically.

	### llama.cpp

	```bash
	llama-cli -m AutoDevvLM-Q4_K_M.gguf -cnv \
	-sys "You are AutoDevvLM, a helpful coding assistant."
	```

	### Ollama

	Create a `Modelfile`:

	```
	FROM ./AutoDevvLM-Q4_K_M.gguf
	SYSTEM """You are AutoDevvLM, a coding assistant. Help with writing, debugging and explaining code."""
	PARAMETER temperature 0.2
	```

	Then:

	```bash
	ollama create autodevvlm -f Modelfile
	ollama run autodevvlm
	```

	### Python (llama-cpp-python)

	```python
	from llama_cpp import Llama

	llm = Llama(model_path="AutoDevvLM-Q4_K_M.gguf", n_ctx=4096)

	out = llm.create_chat_completion(
	messages=[
	{"role": "system", "content": "You are AutoDevvLM, a helpful coding assistant."},
	{"role": "user", "content": "Write a Python function that checks whether a string is a valid IPv4 address."},
	],
	max_tokens=400,
	temperature=0.2,
	top_p=0.95,
	)
	print(out["choices"][0]["message"]["content"])
	```

	Suggested sampling settings: temperature 0.2 to 0.4, top_p 0.95 for code.

	## Training Details

	### Training data

	[`m-a-p/CodeFeedback-Filtered-Instruction`](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) (Apache 2.0). A random subset of 8,000 examples was used after filtering out examples longer than 1,024 tokens, with a 2% hold-out for evaluation loss. Each example was formatted with the Qwen chat template (user query, assistant answer).

	### Training procedure

	\| Setting \| Value \|
	\|---\|---\|
	\| Method \| QLoRA (4-bit NF4 with double quantization, via `bitsandbytes`) \|
	\| Compute precision \| FP16 (BF16 not used; NVIDIA T4 lacks native BF16) \|
	\| LoRA rank / alpha / dropout \| 16 / 32 / 0.05 \|
	\| LoRA target modules \| `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` \|
	\| Epochs \| 1 \|
	\| Batch size \| 2 per device x 8 gradient accumulation (effective 16) \|
	\| Learning rate \| 2e-4, cosine schedule, 3% warmup \|
	\| Optimizer \| `paged_adamw_8bit` \|
	\| Max grad norm \| 0.3 \|
	\| Max sequence length \| 1,024 \|
	\| Gradient checkpointing \| Enabled \|
	\| Hardware \| Google Colab, 1x NVIDIA Tesla T4 (16 GB) \|
	\| Frameworks \| Transformers, PEFT, TRL, bitsandbytes \|

	### Export

	The LoRA adapter was merged into the FP16 base model, converted to GGUF with llama.cpp's `convert_hf_to_gguf.py`, and quantized to Q4_K_M with `llama-quantize`.

	## Evaluation

	No formal benchmark evaluation has been run for this model yet. Results on HumanEval, MBPP or similar benchmarks have not been measured, and no claim is made that it outperforms the base model. Users are encouraged to compare outputs against `Qwen2.5-1.5B-Instruct` on their own tasks.

	## Limitations and Risks

	- Small model: at 1.5B parameters it can produce incorrect, incomplete or insecure code, and may hallucinate APIs or library functions. Always review and test its output.
	- Light fine-tune: training used 8,000 examples for a single epoch, so behavior is close to the base model, with modest changes in code style and explanation format.
	- Identity: the training data contained no identity examples, so the model may state that it is Qwen. Use a system prompt to set the assistant name.
	- Quantization: Q4_K_M trades some quality for size; use the F16 file if you need maximum fidelity.
	- Inherited biases: the model inherits the limitations and biases of the base model and the training dataset.
	- Language coverage: performance is strongest in English and in popular programming languages.

	## License

	Released under the Apache 2.0 license, the same as the base model and the training dataset. Please retain the license notice and attribution when redistributing.

	## Acknowledgements

	- [Qwen Team](https://huggingface.co/Qwen) for the base model, Qwen2.5-1.5B-Instruct
	- [m-a-p](https://huggingface.co/m-a-p) for the CodeFeedback-Filtered-Instruction dataset
	- [llama.cpp](https://github.com/ggml-org/llama.cpp), [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl) and [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes)

	## Citation

	```bibtex
	@misc{autodevvlm2026,
	title = {AutoDevvLM: A QLoRA fine-tune of Qwen2.5-1.5B-Instruct for code assistance},
	author = {<your-name-or-org>},
	year = {2026},
	howpublished = {\url{https://huggingface.co/<your-username>/AutoDevvLM}}
	}
	```