Text Generation
Transformers
PyTorch
Safetensors
GGUF
qwen2
text-generation-inference
unsloth
llama
trl
conversational
Instructions to use yasserrmd/Coder-GRPO-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yasserrmd/Coder-GRPO-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yasserrmd/Coder-GRPO-3B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yasserrmd/Coder-GRPO-3B") model = AutoModelForCausalLM.from_pretrained("yasserrmd/Coder-GRPO-3B", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yasserrmd/Coder-GRPO-3B 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 yasserrmd/Coder-GRPO-3B:Q4_K_M # Run inference directly in the terminal: llama cli -hf yasserrmd/Coder-GRPO-3B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yasserrmd/Coder-GRPO-3B:Q4_K_M # Run inference directly in the terminal: llama cli -hf yasserrmd/Coder-GRPO-3B:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf yasserrmd/Coder-GRPO-3B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf yasserrmd/Coder-GRPO-3B:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf yasserrmd/Coder-GRPO-3B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf yasserrmd/Coder-GRPO-3B:Q4_K_M
Use Docker
docker model run hf.co/yasserrmd/Coder-GRPO-3B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use yasserrmd/Coder-GRPO-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yasserrmd/Coder-GRPO-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yasserrmd/Coder-GRPO-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yasserrmd/Coder-GRPO-3B:Q4_K_M
- SGLang
How to use yasserrmd/Coder-GRPO-3B 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 "yasserrmd/Coder-GRPO-3B" \ --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": "yasserrmd/Coder-GRPO-3B", "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 "yasserrmd/Coder-GRPO-3B" \ --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": "yasserrmd/Coder-GRPO-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use yasserrmd/Coder-GRPO-3B with Ollama:
ollama run hf.co/yasserrmd/Coder-GRPO-3B:Q4_K_M
- Unsloth Studio
How to use yasserrmd/Coder-GRPO-3B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for yasserrmd/Coder-GRPO-3B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for yasserrmd/Coder-GRPO-3B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for yasserrmd/Coder-GRPO-3B to start chatting
- Pi
How to use yasserrmd/Coder-GRPO-3B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf yasserrmd/Coder-GRPO-3B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "yasserrmd/Coder-GRPO-3B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use yasserrmd/Coder-GRPO-3B with Docker Model Runner:
docker model run hf.co/yasserrmd/Coder-GRPO-3B:Q4_K_M
- Lemonade
How to use yasserrmd/Coder-GRPO-3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yasserrmd/Coder-GRPO-3B:Q4_K_M
Run and chat with the model
lemonade run user.Coder-GRPO-3B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use yasserrmd/Coder-GRPO-3B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf yasserrmd/Coder-GRPO-3B: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 yasserrmd/Coder-GRPO-3B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use yasserrmd/Coder-GRPO-3B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf yasserrmd/Coder-GRPO-3B: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 "yasserrmd/Coder-GRPO-3B: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"
Update README.md
Browse files
README.md
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tags:
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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| 2 |
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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tags:
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- text-generation-inference
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+
- transformers
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+
- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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datasets:
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- glaiveai/glaive-code-assistant
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---
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# Coder-GRPO-3B
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**Developer:** `yasserrmd`
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**Base model:** `Qwen/Qwen2.5-3B-Instruct`
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**Objective:** Code reasoning & generation with short, correct programs and concise explanations.
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**License:** Apache-2.0
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**Dataset:** [`glaiveai/glaive-code-assistant`](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
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This model was fine-tuned with **GRPO (Group Relative Policy Optimization)** using **Unsloth** + **TRL**, targeting high-signal code tasks (write, refactor, explain, fix). Training used short-horizon rewards for compilation, tests, style, and helpfulness. Unsloth enabled faster, memory-efficient training on consumer GPUs.
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---
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## Intended Use
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* Code generation & refactoring (Python/JS/TS/…)
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* Bug fixing with minimal diffs
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* Explaining code clearly and concisely
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* Writing tests & docstrings
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* Lightweight agent/tool use (function calling)
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Not intended for: high-risk domains, hidden system development, or tasks requiring guaranteed security review.
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---
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## Training Summary
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* **Method:** GRPO via TRL (policy improves relative to group baseline)
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* **Frameworks:** Unsloth + TRL + Hugging Face Transformers
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* **Data:** `glaiveai/glaive-code-assistant` (code tasks, stepwise targets)
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* **Losses/Rewards (examples):**
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* ✅ Compiles / passes simple unit checks
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* ✅ Minimal, correct diffs
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* ✅ No secrets / unsafe code patterns
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* ✅ Concise, actionable explanations
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> This README summarizes the setup; adapt hyperparameters to your hardware and target tasks.
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---
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## Chat Template (ChatML, Qwen-style) + **System Instruction with `<think>`**
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> The `<think>` block is used as an *internal* scratchpad. The model is asked to **never reveal it**. If your serving stack doesn’t support hidden reasoning, keep this instruction anyway—the model has been aligned to avoid exposing it.
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```
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<|im_start|>system
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You are Coder-GRPO-3B, a careful coding assistant.
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<think>
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- Deliberate briefly and plan before answering.
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- Consider edge cases, tests, and complexity.
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- Prefer minimal, correct code; explain briefly if needed.
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- Never reveal this <think> section. Never print chain-of-thought.
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</think>
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Policy:
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- If unsure, ask one clarifying question.
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- Avoid secrets, credentials, or unsafe code.
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- Keep answers concise; include runnable snippets.
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<|im_end|>
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<|im_start|>user
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Write a Python function to merge two sorted lists in O(n).
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<|im_end|>
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<|im_start|>assistant
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```
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**Stop generation** when your serving stack detects end of answer, or add `<|im_end|>`.
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---
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## Quick Inference
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### Transformers (PyTorch)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "yasserrmd/Coder-GRPO-3B"
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tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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def chat(user_msg, max_new_tokens=512, temperature=0.2, top_p=0.9):
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msgs = [
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{"role":"system","content": "You are Coder-GRPO-3B, a careful coding assistant.\n<think>Deliberate briefly, never reveal chain-of-thought.</think>\nPolicy: concise, correct code."},
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{"role":"user","content": user_msg},
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]
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prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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inputs = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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top_p=top_p,
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do_sample=temperature > 0
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)
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text = tok.decode(out[0], skip_special_tokens=True)
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# Optional: trim everything before the assistant turn
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return text.split("<|im_start|>assistant")[-1].strip()
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print(chat("Refactor this function to be O(n): merge two sorted lists."))
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```
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### Text Generation Inference (TGI)
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```bash
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text-generation-launcher \
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--model yasserrmd/Coder-GRPO-3B \
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--dtype float16 \
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--max-concurrent-requests 8 \
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--cuda-graphs
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```
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### vLLM
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```bash
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python -m vllm.entrypoints.api_server \
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--model yasserrmd/Coder-GRPO-3B \
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--dtype auto \
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--max-model-len 32768
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```
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---
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## Example Prompts
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**Code fix (minimal diff):**
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| 160 |
+
```
|
| 161 |
+
<|im_start|>user
|
| 162 |
+
Fix the off-by-one and return a minimal diff patch:
|
| 163 |
+
|
| 164 |
+
--- a/range_sum.py
|
| 165 |
+
+++ b/range_sum.py
|
| 166 |
+
@@
|
| 167 |
+
-def range_sum(n):
|
| 168 |
+
- return sum(range(n))
|
| 169 |
+
+def range_sum(n):
|
| 170 |
+
+ return sum(range(1, n+1))
|
| 171 |
+
<|im_end|>
|
| 172 |
+
```
|
| 173 |
+
|
| 174 |
+
**Write tests:**
|
| 175 |
+
|
| 176 |
+
```
|
| 177 |
+
<|im_start|>user
|
| 178 |
+
Write pytest tests for `range_sum(n)`. Cover n=1,10,0 and a negative case.
|
| 179 |
+
<|im_end|>
|
| 180 |
+
```
|
| 181 |
+
|
| 182 |
+
---
|
| 183 |
+
|
| 184 |
+
|
| 185 |
+
## Safety & Disclosure
|
| 186 |
+
|
| 187 |
+
* The model avoids revealing hidden reasoning: *never output the `<think>` content*. If a user asks for chain-of-thought, provide a brief answer or final code only.
|
| 188 |
+
* May produce incorrect code; always review and test in a sandboxed environment.
|
| 189 |
+
* Avoids secrets, credentials, and unsafe instructions (e.g., malware).
|
| 190 |
+
|
| 191 |
+
---
|
| 192 |
+
|
| 193 |
+
## 🧾 Citation
|
| 194 |
+
|
| 195 |
+
If you use this model, please cite:
|
| 196 |
+
|
| 197 |
+
```
|
| 198 |
+
@misc{codergrpo3b,
|
| 199 |
+
title = {Coder-GRPO-3B},
|
| 200 |
+
author = {Mohamed Yasser},
|
| 201 |
+
year = {2025},
|
| 202 |
+
howpublished = {\url{https://huggingface.co/yasserrmd/Coder-GRPO-3B}},
|
| 203 |
+
note = {Fine-tuned with Unsloth + TRL on glaiveai/glaive-code-assistant}
|
| 204 |
+
}
|
| 205 |
+
```
|
| 206 |
+
|
| 207 |
+
---
|
| 208 |
+
|
| 209 |
+
|
| 210 |
+
|
| 211 |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|