Instructions to use yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k with Transformers:

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

pipe = pipeline("text-generation", model="yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k")
model = AutoModelForCausalLM.from_pretrained("yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k

SGLang

How to use yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k 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 "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k" \
    --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": "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k",
		"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 "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k" \
        --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": "yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k with Docker Model Runner:
```
docker model run hf.co/yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k
```

yeok commited on Mar 31, 2025

Commit

55ad3fe

verified ·

1 Parent(s): bc40756

End of training

Browse files

Files changed (4) hide show

README.md +5 -3
all_results.json +5 -0
config.json +29 -0
eval_results.json +7 -0

README.md CHANGED Viewed

@@ -1,17 +1,19 @@
 ---
 base_model: Qwen/Qwen2.5-1.5B-Instruct
 library_name: transformers
-model_name: qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k
 tags:
 - generated_from_trainer
 - trl
 - sft
 licence: license
 ---
-# Model Card for qwen-2.5-1.5B-instruct-sft-lora-countdown-search-5k
-This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

 ---
 base_model: Qwen/Qwen2.5-1.5B-Instruct
+datasets: MelinaLaimon/stream-of-search
 library_name: transformers
+model_name: Qwen/Qwen2.5-1.5B-Instruct
 tags:
 - generated_from_trainer
+- alignment-handbook
 - trl
 - sft
 licence: license
 ---
+# Model Card for Qwen/Qwen2.5-1.5B-Instruct
+This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [MelinaLaimon/stream-of-search](https://huggingface.co/datasets/MelinaLaimon/stream-of-search) dataset.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

all_results.json CHANGED Viewed

@@ -1,4 +1,9 @@
 {
     "total_flos": 2.4518665439700582e+17,
     "train_loss": 0.05413007871165959,
     "train_runtime": 8945.5939,

 {
+    "eval_loss": 0.04217606037855148,
+    "eval_runtime": 980.4186,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 3.163,
+    "eval_steps_per_second": 1.582,
     "total_flos": 2.4518665439700582e+17,
     "train_loss": 0.05413007871165959,
     "train_runtime": 8945.5939,

config.json ADDED Viewed

	@@ -0,0 +1,29 @@

+{
+  "_attn_implementation_autoset": true,
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

eval_results.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+    "eval_loss": 0.04217606037855148,
+    "eval_runtime": 980.4186,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 3.163,
+    "eval_steps_per_second": 1.582
+}