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Model: DireDreadlord/GemCod-Jade-270M
Source: Original Platform

README.md

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+---
+license: gemma
+datasets:
+- DireDreadlord/magicoder-glaive-code-instruct
+language:
+- en
+base_model:
+- google/gemma-3-270m-it
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+- code
+- gemma3
+- SLM
+- chat
+---
+
+
+# GemCod270M - Jade (gemma-270m-it-code v4.1.0)
+
+![GemCod logo](./gemcod_logo_c.png)
+                                                                                                                                
+GemCod is a lightweight code generation model finetuned using SFT on the base gemma-270m-it model(https://huggingface.co/google/gemma-3-270m-it). It offers accurate and quick(ish) code snippet and long-form code generation in all major programming languages.
+It's small size (270M parameters) allows it to run comfortably on laptop grade GPUs.
+
+The Jade model serves as an upgrade from the previous Aquamarine model(https://huggingface.co/DireDreadlord/GemCod-codegen-270M), as it provides facilities for superior long-form code generation and explainability of generated snippets whilst keeping the inference time and space requirements roughly the same.
+
+This model also offers rudimentary Q/A and subject matter expert capabilities on code related subjects.
+
+---
+
+
+**Estimated parameters:** ~270M
+
+**Architecture:** Gemma3
+
+**Intended use:** Code snippet and long-form code generation from natural language
+
+---
+
+
+## Training data
+- Source: magicoder-glaive-code-instruct dataset (https://huggingface.co/datasets/DireDreadlord/magicoder-glaive-code-instruct)
+- Rows: ~350,000 rows templated with a custom .jinja chat format
+- Training: trained for 3,000 steps on an RTX 3050 (4GB VRAM)
+
+
+## Usage
+
+Install requirements:
+
+```bash
+pip install -r requirements.txt
+pip install transformers datasets accelerate safetensors
+```
+
+
+## Usage (Hugging Face Hub)
+You can load it directly from HuggingFace:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+tokenizer = AutoTokenizer.from_pretrained("DireDreadlord/GemCod-Jade-270M")
+model = AutoModelForCausalLM.from_pretrained("DireDreadlord/GemCod-Jade-270M")
+model.to(device)
+model.eval()
+model.resize_token_embeddings(len(tokenizer))
+
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+chat_template = """{% for message in messages %}{% if message['role'] == 'user' %}User: {{ message['content'] }}
+{% elif message['role'] == 'assistant' %}Assistant: {{ message['content'] }}
+{% endif %}{% endfor %}"""
+tokenizer.chat_template = chat_template
+
+def generate_code(prompt, max_tokens) -> str:
+    messages = [
+        {
+            "role": "user",
+            "content": prompt
+        }
+    ]
+    
+    formatted_prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True 
+    )
+
+    
+    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
+    input_length = inputs["input_ids"].shape[1]
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            do_sample=False,
+            num_beams=1,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            use_cache=False,
+        )
+
+    generated_tokens = outputs[0][input_length:]
+    generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+    return generated_text
+
+
+prompt = "WAP to train a sklearn model to predict the price of a house based on its size and location"
+print("Prompt: ", prompt)
+
+result = generate_code(prompt)
+print(result)
+```
+
+
+## Limitations
+- Model for experimental use only; users should employ it as such under license.