Instructions to use banty1614/codeforge-qwen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use banty1614/codeforge-qwen-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "banty1614/codeforge-qwen-lora")

Transformers

How to use banty1614/codeforge-qwen-lora with Transformers:

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

pipe = pipeline("text-generation", model="banty1614/codeforge-qwen-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("banty1614/codeforge-qwen-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use banty1614/codeforge-qwen-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "banty1614/codeforge-qwen-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "banty1614/codeforge-qwen-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/banty1614/codeforge-qwen-lora

SGLang

How to use banty1614/codeforge-qwen-lora 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 "banty1614/codeforge-qwen-lora" \
    --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": "banty1614/codeforge-qwen-lora",
		"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 "banty1614/codeforge-qwen-lora" \
        --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": "banty1614/codeforge-qwen-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use banty1614/codeforge-qwen-lora with Docker Model Runner:
```
docker model run hf.co/banty1614/codeforge-qwen-lora
```

banty1614 commited on Jun 29

Commit

3f0b298

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1 Parent(s): 978587c

Update README.md

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@@ -1,3 +1,44 @@
 # 🚀 CodeForge AI
 > A QLoRA fine-tuned version of **Qwen2.5-3B-Instruct** for software engineering and Python coding tasks.

+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+language:
+  - en
+datasets:
+  - iamtarun/python_code_instructions_18k_alpaca
+tags:
+  - qwen2
+  - qwen
+  - qlora
+  - lora
+  - peft
+  - transformers
+  - instruction-tuning
+  - code-generation
+  - python
+  - fine-tuning
+model-index:
+  - name: CodeForge AI
+    results:
+      - task:
+          type: text-generation
+        metrics:
+          - type: train_loss
+            value: 0.7047
+            name: Training Loss
+---
+# 🚀 CodeForge AI
+> Fine-tuned Qwen2.5-3B-Instruct using QLoRA + PEFT for software engineering tasks.
 # 🚀 CodeForge AI
 > A QLoRA fine-tuned version of **Qwen2.5-3B-Instruct** for software engineering and Python coding tasks.