Instructions to use beyoru/ThinkAgain1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/ThinkAgain1.5 with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/ThinkAgain1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/ThinkAgain1.5")
model = AutoModelForCausalLM.from_pretrained("beyoru/ThinkAgain1.5", 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 beyoru/ThinkAgain1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/ThinkAgain1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/ThinkAgain1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/ThinkAgain1.5

SGLang

How to use beyoru/ThinkAgain1.5 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 "beyoru/ThinkAgain1.5" \
    --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": "beyoru/ThinkAgain1.5",
		"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 "beyoru/ThinkAgain1.5" \
        --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": "beyoru/ThinkAgain1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/ThinkAgain1.5 with Docker Model Runner:
```
docker model run hf.co/beyoru/ThinkAgain1.5
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+78

-66

Files changed (1) hide show

README.md +78 -66

README.md CHANGED Viewed

@@ -1,67 +1,79 @@
----
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- qwen2
-- trl
-- sft
-license: apache-2.0
-language:
-- en
----
-### Model detail
-Reasoning natural and smarter\
-No system prompt training\
-LoRA training rank 16 and alpha 16\
-Tool calling support
-### Usage:
-```
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-MAX_REASONING_TOKENS = 4096
-MAX_RESPONSE_TOKENS = 1024
-model_name = "beyoru/ThinkAgain1.5"
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-messages = []
-def stream_output(output_text):
-    for char in output_text:
-        print(char, end="", flush=True)
-while True:
-    prompt = input("USER: ")
-    messages.append({"role": "user", "content": prompt})
-    # Generate reasoning
-    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
-    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
-    reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
-    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-    messages.append({"role": "reasoning", "content": reasoning_output})
-    print("REASONING: ", end="")
-    stream_output(reasoning_output)
-    print()
-    # Generate answer
-    response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
-    response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
-    response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-    messages.append({"role": "assistant", "content": response_output})
-    print("ASSISTANT: ", end="")
-    stream_output(response_output)
-    print()
 ```

+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- qwen2
+- trl
+- sft
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+### Model detail
+Reasoning natural and smarter\
+No system prompt training\
+LoRA training rank 16 and alpha 16\
+Tool calling support
+### Usage:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+MAX_REASONING_TOKENS = 4096
+MAX_RESPONSE_TOKENS = 1024
+model_name = "beyoru/ThinkAgain1.5"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+messages = []
+def stream_output(output_text):
+    for char in output_text:
+        print(char, end="", flush=True)
+while True:
+    prompt = input("USER: ")
+    messages.append({"role": "user", "content": prompt})
+    # Generate reasoning
+    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+    reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    messages.append({"role": "reasoning", "content": reasoning_output})
+    print("REASONING: ", end="")
+    stream_output(reasoning_output)
+    print()
+    # Generate answer
+    response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+    response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+    response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    messages.append({"role": "assistant", "content": response_output})
+    print("ASSISTANT: ", end="")
+    stream_output(response_output)
+    print()
 ```