Instructions to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B")
model = AutoModelForCausalLM.from_pretrained("ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B

SGLang

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B 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 "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B" \
    --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": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"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 "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B" \
        --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": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with Docker Model Runner:
```
docker model run hf.co/ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B
```

ranjanrajib commited on Sep 16

Commit

b1e2897

verified ·

1 Parent(s): 8954115

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +50 -0

README.md CHANGED Viewed

@@ -20,6 +20,56 @@ attempts, where some samples passed a problem's tests and others failed.
 **Base model:** `Qwen/Qwen2.5-Coder-7B-Instruct` at snapshot
 `c03e6d358207e414f1eca0bb1891e29f1db0e242`. The merge was performed against exactly these weights.
 ## What was measured, and what it does and does not show
 On `python_eval_v3` — 508 BigCodeBench problems held out of training, 10 samples per problem at

 **Base model:** `Qwen/Qwen2.5-Coder-7B-Instruct` at snapshot
 `c03e6d358207e414f1eca0bb1891e29f1db0e242`. The merge was performed against exactly these weights.
+## Downloading and using
+### The merged model (what most people want)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "ranjanrajib/DPO-trained-Qwen2.5-Coder-7B", dtype="bfloat16", device_map="auto")
+tok = AutoTokenizer.from_pretrained("ranjanrajib/DPO-trained-Qwen2.5-Coder-7B")
+msgs = [{"role": "user", "content": "Write a Python function that merges overlapping intervals."}]
+text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+out = model.generate(**tok(text, return_tensors="pt").to(model.device), max_new_tokens=512)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+Or fetch the files without loading them (~14 GB, four shards):
+```bash
+hf download ranjanrajib/DPO-trained-Qwen2.5-Coder-7B --exclude "adapter/*" --local-dir ./d4
+```
+### The LoRA adapter only (308 MB)
+The adapter lives in `adapter/`. Applying it to the base model gives the same result as the
+merged weights, at a fraction of the download.
+```bash
+hf download ranjanrajib/DPO-trained-Qwen2.5-Coder-7B --include "adapter/*" --local-dir ./d4-lora
+```
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+BASE = "Qwen/Qwen2.5-Coder-7B-Instruct"
+REVISION = "c03e6d358207e414f1eca0bb1891e29f1db0e242"   # the snapshot this was trained against
+base = AutoModelForCausalLM.from_pretrained(
+    BASE, revision=REVISION, dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(
+    base, "ranjanrajib/DPO-trained-Qwen2.5-Coder-7B", subfolder="adapter")
+```
+**Pin the revision.** The adapter's own config does not record one, so omitting `revision`
+silently applies it to whatever `main` points at. Today that is the same snapshot; it will not
+stay true forever, and an adapter applied to different base weights is not this model.
 ## What was measured, and what it does and does not show
 On `python_eval_v3` — 508 BigCodeBench problems held out of training, 10 samples per problem at